{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb6aa99c",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 基于昇思MindSpore+Orangepi AIpro的FCN图像语义分割\n",
    "\n",
    "功能：使用基于昇思MindSpore框架开发的FCN模型对输入图片进行语义分割。  \n",
    "样例输入：原始图片。  \n",
    "样例输出：与输入大小相同的图像，输出图像的每个像素对应了输入图像每个像素的类别。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1451fc2e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 前期准备\n",
    "\n",
    "* 基础镜像的样例目录中已包含转换后的om模型以及测试图片，如果直接运行，可跳过此步骤。如果需要重新转换模型，可以参考下面的步骤。\n",
    "* **建议在Linux服务器或者虚拟机转换该模型。**\n",
    "* 为了能进一步优化模型推理性能，我们需要将其转换为om模型进行使用；**转换指导详见全流程实验指导。**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82653e5f",
   "metadata": {},
   "source": [
    "# 模型推理实现\n",
    "\n",
    "* 注意：本案例在离线推理的过程中可能会出现内存不足的问题，可以根据情况查看FAQ文档中的解决方案。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95a2799-c2f5-405f-a4b1-722248655aa8",
   "metadata": {},
   "source": [
    "### 1. 导入三方库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a9e0abe-f4f5-4a29-8af8-4e082955d206",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import mindspore.dataset as ds\n",
    "\n",
    "import acl\n",
    "import acllite_utils as utils\n",
    "from acllite_model import AclLiteModel\n",
    "from acllite_resource import resource_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30643cc9-9015-433b-8c31-66ed14cabdfe",
   "metadata": {},
   "source": [
    "### 2. 定义acllite资源初始化与去初始化类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b096cb19-e3e6-49fd-a38e-f55750f41154",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AclLiteResource:\n",
    "    \"\"\"\n",
    "    AclLiteResource\n",
    "    \"\"\"\n",
    "    def __init__(self, device_id=0):\n",
    "        self.device_id = device_id\n",
    "        self.context = None\n",
    "        self.stream = None\n",
    "        self.run_mode = None\n",
    "        \n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "        init resource\n",
    "        \"\"\"\n",
    "        print(\"init resource stage:\")\n",
    "        ret = acl.init()\n",
    "\n",
    "        ret = acl.rt.set_device(self.device_id)\n",
    "        utils.check_ret(\"acl.rt.set_device\", ret)\n",
    "\n",
    "        self.context, ret = acl.rt.create_context(self.device_id)\n",
    "        utils.check_ret(\"acl.rt.create_context\", ret)\n",
    "\n",
    "        self.stream, ret = acl.rt.create_stream()\n",
    "        utils.check_ret(\"acl.rt.create_stream\", ret)\n",
    "\n",
    "        self.run_mode, ret = acl.rt.get_run_mode()\n",
    "        utils.check_ret(\"acl.rt.get_run_mode\", ret)\n",
    "\n",
    "        print(\"Init resource success\")\n",
    "\n",
    "    def __del__(self):\n",
    "        print(\"acl resource release all resource\")\n",
    "        resource_list.destroy()\n",
    "        if self.stream:\n",
    "            print(\"acl resource release stream\")\n",
    "            acl.rt.destroy_stream(self.stream)\n",
    "\n",
    "        if self.context:\n",
    "            print(\"acl resource release context\")\n",
    "            acl.rt.destroy_context(self.context)\n",
    "\n",
    "        print(\"Reset acl device \", self.device_id)\n",
    "        acl.rt.reset_device(self.device_id)\n",
    "        print(\"Release acl resource success\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ab3af4-e881-432e-a3f2-9461666f6dbc",
   "metadata": {},
   "source": [
    "### 3. 数据预处理\n",
    "由于PASCAL VOC 2012数据集中图像的分辨率大多不一致，无法放在一个tensor中，故输入前需做标准化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69d96008-0e6f-498e-bd5c-324cc5e760c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SegDataset:\n",
    "    def __init__(self,\n",
    "                 image_mean,\n",
    "                 image_std,\n",
    "                 data_file='',\n",
    "                 batch_size=32,\n",
    "                 crop_size=512,\n",
    "                 max_scale=2.0,\n",
    "                 min_scale=0.5,\n",
    "                 ignore_label=255,\n",
    "                 num_classes=21,\n",
    "                 num_readers=2,\n",
    "                 num_parallel_calls=4):\n",
    "\n",
    "        self.data_file = data_file\n",
    "        self.batch_size = batch_size\n",
    "        self.crop_size = crop_size\n",
    "        self.image_mean = np.array(image_mean, dtype=np.float32)\n",
    "        self.image_std = np.array(image_std, dtype=np.float32)\n",
    "        self.max_scale = max_scale\n",
    "        self.min_scale = min_scale\n",
    "        self.ignore_label = ignore_label\n",
    "        self.num_classes = num_classes\n",
    "        self.num_readers = num_readers\n",
    "        self.num_parallel_calls = num_parallel_calls\n",
    "        max_scale > min_scale\n",
    "\n",
    "    def preprocess_dataset(self, image, label):\n",
    "        image_out = cv2.imdecode(np.frombuffer(image, dtype=np.uint8), cv2.IMREAD_COLOR)\n",
    "        label_out = cv2.imdecode(np.frombuffer(label, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)\n",
    "        sc = np.random.uniform(self.min_scale, self.max_scale)\n",
    "        new_h, new_w = int(sc * image_out.shape[0]), int(sc * image_out.shape[1])\n",
    "        image_out = cv2.resize(image_out, (new_w, new_h), interpolation=cv2.INTER_CUBIC)\n",
    "        label_out = cv2.resize(label_out, (new_w, new_h), interpolation=cv2.INTER_NEAREST)\n",
    "\n",
    "        image_out = (image_out - self.image_mean) / self.image_std\n",
    "        out_h, out_w = max(new_h, self.crop_size), max(new_w, self.crop_size)\n",
    "        pad_h, pad_w = out_h - new_h, out_w - new_w\n",
    "        if pad_h > 0 or pad_w > 0:\n",
    "            image_out = cv2.copyMakeBorder(image_out, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=0)\n",
    "            label_out = cv2.copyMakeBorder(label_out, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=self.ignore_label)\n",
    "        offset_h = np.random.randint(0, out_h - self.crop_size + 1)\n",
    "        offset_w = np.random.randint(0, out_w - self.crop_size + 1)\n",
    "        image_out = image_out[offset_h: offset_h + self.crop_size, offset_w: offset_w + self.crop_size, :]\n",
    "        label_out = label_out[offset_h: offset_h + self.crop_size, offset_w: offset_w+self.crop_size]\n",
    "        if np.random.uniform(0.0, 1.0) > 0.5:\n",
    "            image_out = image_out[:, ::-1, :]\n",
    "            label_out = label_out[:, ::-1]\n",
    "        image_out = image_out.transpose((2, 0, 1))\n",
    "        image_out = image_out.copy()\n",
    "        label_out = label_out.copy()\n",
    "        label_out = label_out.astype(\"int32\")\n",
    "        return image_out, label_out\n",
    "\n",
    "    def get_dataset(self):\n",
    "        ds.config.set_numa_enable(True)\n",
    "        dataset = ds.MindDataset(self.data_file, columns_list=[\"data\", \"label\"],\n",
    "                                 shuffle=True, num_parallel_workers=self.num_readers)\n",
    "        transforms_list = self.preprocess_dataset\n",
    "        dataset = dataset.map(operations=transforms_list, input_columns=[\"data\", \"label\"],\n",
    "                              output_columns=[\"data\", \"label\"],\n",
    "                              num_parallel_workers=self.num_parallel_calls)\n",
    "        dataset = dataset.shuffle(buffer_size=self.batch_size * 10)\n",
    "        dataset = dataset.batch(self.batch_size, drop_remainder=True)\n",
    "        return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "014b1b23-255d-4ed0-af3b-2fa9a4e1f0a8",
   "metadata": {},
   "source": [
    "### 4. 前处理、推理、后处理所需要的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc6fd38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from download import download\n",
    "\n",
    "# 获取数据集\n",
    "dataset_url = \"https://modelers.cn/coderepo/web/v1/file/MindSpore-Lab/cluoud_obs/main/media/examples/mindspore-courses/orange-pi-mindspore/07-FCN/data.zip\"\n",
    "download(dataset_url, \"./\", kind=\"zip\", replace=True)\n",
    "\n",
    "# 获取模型om文件\n",
    "model_url = \"https://modelers.cn/coderepo/web/v1/file/MindSpore-Lab/cluoud_obs/main/media/examples/mindspore-courses/orange-pi-mindspore/07-FCN/FCN8s.zip\"\n",
    "download(model_url, \"./\", kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f88ecd09-1ce9-4ef8-a7de-88da8e9e9a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义处理数据集的参数\n",
    "IMAGE_MEAN = [103.53, 116.28, 123.675]\n",
    "IMAGE_STD = [57.375, 57.120, 58.395]\n",
    "DATA_FILE = \"data/VOC_mindrecord\"\n",
    "\n",
    "# 定义模型推理参数\n",
    "eval_batch_size = 1\n",
    "crop_size = 512\n",
    "min_scale = 0.5\n",
    "max_scale = 2.0\n",
    "ignore_label = 255\n",
    "num_classes = 21\n",
    "\n",
    "@utils.display_time\n",
    "def pre_process():\n",
    "    \"\"\"\n",
    "    image preprocess\n",
    "    \"\"\"\n",
    "    # 实例化Dataset\n",
    "    dataset = SegDataset(image_mean=IMAGE_MEAN,\n",
    "                         image_std=IMAGE_STD,\n",
    "                         data_file=DATA_FILE,\n",
    "                         batch_size=eval_batch_size,\n",
    "                         crop_size=crop_size,\n",
    "                         max_scale=max_scale,\n",
    "                         min_scale=min_scale,\n",
    "                         ignore_label=ignore_label,\n",
    "                         num_classes=num_classes,\n",
    "                         num_readers=2,\n",
    "                         num_parallel_calls=4)\n",
    "\n",
    "    dataset = dataset.get_dataset()\n",
    "    return dataset\n",
    "\n",
    "# 图片推理\n",
    "@utils.display_time\n",
    "def inference(dataset, model):\n",
    "    \"\"\"\n",
    "    model inference\n",
    "    \"\"\"\n",
    "    show_data = next(dataset.create_dict_iterator())\n",
    "    show_images = show_data[\"data\"].asnumpy()\n",
    "    mask_images = show_data[\"label\"].reshape([1, 512, 512])\n",
    "    show_images = np.clip(show_images, 0, 1)\n",
    "    result = model.execute([show_data[\"data\"].asnumpy(), ])\n",
    "    res = result[0].argmax(axis=1)\n",
    "    return res ,show_images\n",
    "\n",
    "@utils.display_time\n",
    "def post_process(res, show_images):\n",
    "    \"\"\"\n",
    "    post process\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.imshow(show_images[0].transpose(1, 2, 0))\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplots_adjust(wspace=0.05, hspace=0.02)\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.imshow(res.transpose(1, 2, 0))\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplots_adjust(wspace=0.05, hspace=0.02)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafe334c-93d9-4469-b7e3-55e588018f82",
   "metadata": {},
   "source": [
    "### 5. 构造主函数，串联整个代码逻辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e3a384d",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.getcwd()\n",
    "def main():\n",
    "    # acl初始化\n",
    "    acl_resource = AclLiteResource()\n",
    "    acl_resource.init()\n",
    "    # 加载模型\n",
    "    model_path = os.path.join(path, \"FCN8s.om\")\n",
    "    model = AclLiteModel(model_path)\n",
    "    # preprocess\n",
    "    dataset = pre_process()   # 前处理\n",
    "    # inference\n",
    "    res, show_images = inference(dataset, model)   # 推理\n",
    "    # postprocess\n",
    "    post_process(res, show_images)  # 后处理    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd648da3-8d25-47d8-ace2-24fdd674dfa5",
   "metadata": {},
   "source": [
    "### 6. 运行\n",
    "运行完成后，会显示推理前和推理后的图片，如下所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "396dab1a-ced7-4b68-841c-810d0b3b65fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init resource stage:\n",
      "Init resource success\n",
      "Init model resource start...\n",
      "[AclLiteModel] create model output dataset:\n",
      "malloc output 0, size 22020096\n",
      "Create model output dataset success\n",
      "Init model resource success\n",
      "in pre_process, use time:0.004704952239990234\n",
      "in inference, use time:0.6202666759490967\n"
     ]
    },
    {
     "data": {
      "image/png": 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7d6/ZjdnXLW0NlwZxc0Rz4e4+Iv2hrGbP+DOX0pHsINlwlOtNWx7DKNUVQRmlZduWRU97zxFvowA3XH4qsYP13HGFiz8+3XJI8jg0CHfE6O2xKC/xUlziZPO2AQA8Hp3f/PxyZhXncePtD/GNbx/7uY6TkWzAoE+kefnAft687xUSG0L0mi2I7hiptI1L8+E38/CHnFgN/fimejnvY6ezqqcFV0EeqVVbUUwby4JYAixDYWKByrQCjTferae8zImt2EyckI8/4Gdj95B437ymlYlFe/jIjcu491/P0NueJtxj484tZ8Els9n+1kbIcVBkajz05DtsHecjr1Bj2XkVLJieQ19ThKZyL4nxpaz88xZ61o1tKI9Fqs+Ds7wU57I0ZleYwK0L2PDW2xDVcNsF2G93IQrSJH2FoPthRxecnQsXfwQ2b6c3nqDAgCt/8zCqbfPcww+R6tkBZgzOnAKb26A1BLEweDP+ebudQT3KSBm8vbueVx/5D8nWFlCKoOAk6OqUDGUe+Vm0gjK0mmmk1y1HxBPypBmFnhBYBppXw3JoUFYM4bRkftsi1dXP3Xd+FjMSkt1YVAmaAes6GG2YCNM6rN2xSNEVHCmFZCpz/XDTd/Ra4HJI6Rc3SHfGs7r/cMp16Tz86HrOnqQzo8CJ360RTsh2DRPShkqh38E3bjkTvaSQL371IT798QX855ldPP3Wbl7CR9NoaXUEypqRBGkSdhs7Qlt4pbWRqDNAck8Y0ZlG2AbzFi4gFN6A2JNk6lk1RMubaHpiGyJhErjOw+0Xf5TX12wk8UYLVjJJYW4O7e196ArEQgathkLESPPvV/bj8EA8blBa7h/Rh1TC4pHHlhMschAJGcy6fgJ5hSW89IeVeE7yEW+P4vI4SOYopPrSvLYpSVW1n+9/9+tUldfS3bCW365/kafe2EHf3sNXRFUD+wjjNtDZzcD9vShOB6IzzcDGHrCLYW8TSS0GkQQINzyzBtY2g23D0omwygm9CRzxHn5ZMo3FuQX87PWnMP7+L/jcTFAU3JOmoEw/i8Rv/gBGGtr3DPYI1BzQA1iJg/z0xlswEwl5zjEZYl1QMQU62iF6ZEaywr0ozXUjT3oExEwcLicXfu0mXnr0ZYwlS1EPtGG/9g7gAEXF7OmVLlGfA+I2pJMyBvceqfH1VWN/oQIWqDl+7HgCTAtUBaU4F9EeAjM99u+OQh63k1TKoOrsc9m9dRcuVxskhl6w2+vG67FYeu6F1HfHKM93cNddv6Cz90s8+JdNx2UHZs1IjzU/wp7QPt7auI6k4eTD530Uxxev59lnHsMsdrN69WoSmoExu5jXX3iCM08+HWXjQRpe2cWmp9ZTFajmhpql9JdsZNc7dRgTLBQdnDZ0JaXNYQM7GvtBgKopxIRN+eRiFp2ykO6ebja8u5lUj0H/gIGqQ82EicyePpfXH1uHbSZZelotfb0xZp86lVneYvq3hvjTP17jV798nDPPLUN15bBhZw9mwIdw9YICjjIweoA0iLGCsYPUCRRrKLYPYZuIlm60kmKsPhtCKQj6QHFAqwnFeeDvIzglQbjtIPbMWlzTTqfcE+CZdW/xl8cewc7zE5h4No6L8hh4djXO7/4ASsqg5QDgQLqQLOlutpwAmLFYpjMhSG2WgVZLlR69o5GRxmxvHnluQIoUw0zz6m8fxYjEwbMee297ZmY4IGcc9O2BlA1pG7a0yuBvdot0djQ4WTP3dAR82EJgRGKQSuMtyCeZMLFix89I7aE440qcrH5mBc+82053dMjmyvc5yXc76FfSJJvXYVNNW4/B9374Gzbu6DrE2NmSIkR2xU+0U7w4Ey4WfP4MamdOppACasdN45k9b/Dq5+6hYGkpTAyQN20m7S9vY46nmgpvkEe//2+EJVBUhfwKH8WlfvZs7EAooOlgp6R0H5zDrlxIpUG1FcnmJuTn55FIxnGMd3LqBYt488U1xJrjFObm8sjzf+W3//ozgdJcYvt7mTd1Ipu27aY8t4hVT29l5z45Mdx+BUMRWAY45wdIt6cQDWm0CeD3uVDTCv17k4ybVUOkN0J/W9fIAQgAuo6+uBZzoBma4lAbhK441Kco/NRtiNqZ9L7wIuREUYqjVH/iU3St6yYRyid/4XwW79/Iql/8glBLJ0yppej332FZoITHzr4Ye/JU2LYD0ikOPTiA2wEp87A4ySFSdelYGIMcwFSfm/3xJPFjvWUFGO+FngS4BXQf43qAoAc0ZGzrRFKpU0q/iPm+vIMAOR6Nqxbkccops/j0L1aQzky0iUEHX71mAtv293Lq5VP49d/3smFn79APB1VOAdmwSNZxJPugQXLzAKvufI4nfvxfXu/dxw/u/iF9Gw4y+Yxp+DSd/ld20fH0CoSjj/X/eZ2GTbtxeRzoHgXNDWkjje7RcHpls1Pm5eP2qThUqMqRf1NxZIwHga/SgbAFPe19xAaSLL3mZBbdchYll9cicgW9HSH+858n8JS6SIR1KqbOorJmGrNn1fDkk2+wq0kyrHBBIiIwIyBSYMTSCGFDhYYVVQjVm0T6FDSPk7u++m2+863voQyH4KgqRICoidnQCOPzwNBQSyqhzQKPC/f0GSy67CqUHgF9HThnziW2pZP0g08jnn2Y3p9+ixfv+DyhhoNycpQUEVu3jqb+ARk32rAxw0SAU0ogPG4cJ88/IhMpufno885guKFRUlNBbU0RDgUW6y4Cln5sJgLZp6Y4esCDe+p4tJMrwAmBaZUovozECzigeFiEMpKEWHLsoCmg5uWg+HxIbjsO6kzjmF6B4sm063TI4z1QOGGxYkeMUy+7hLtunEGBX95n1rwJfOQ7d1IRtPnnPesptSKoQEmBgqpKZ0R+wHko4H4syt7Z0C9XPZGyiYpOtv7rCcSzYXpdO3HlOgh6ba6+oJpxtcXUN7XQNktj40ubSKekM0jPVXAXOdi+qhVVg5LpKsGyIJdMqeaFR7YytVBj4ewcntk0QNH8Iro39JHqMsmdpBNqtnDM8HOgt5NTvJUkS/JRTvJib0vy7z8/y4yLx7PhP/twBz28NbmC624+l4/cfDkbduyltV/QnLSxdjahhFKItIVI26jLfNhNCbRUDqWLF9Hxn3cgKuhs62XL228jBt07qkJg6hQi9Y1ohR6qrz+dpu1bsRM2Yl8HmAKCGp1+Qf+uHQiXC/c111N6zmwO/PYxRDQB9XsgnZCT1eWWDLN1FfF3V7Gu7DFw6ZDvglwPtITAVOSgpQ3srvBQ3MatQ3kRNLaDgMDUKcz48BWs2bYSDANFVfnUxz9Nvkjx/e/+iFLNyyvJ7Lxp8uWC2ZHAN7cQT9DHQHuUSFMHJDISr9gF3cNc7VbGBX4kh2EskRnHUTrz6DiUmjk3qEoJcJs2EcMcut4adQ+fG3J80N7LsagtkuKf97zEmeeeSmOrwSOv7yMYcOIOnsQNn7uBOydOpvdAnMUXfYOTq32IGpULz5/LL/+yhpSanazJmpF8bkgkAA0UoWBZNkQEqViC1ECC0y6bwreuu54DrVugv4uFZ81istbO2+u7aWhNYIYE3QMxKSoVEH0qjavbWfbxKbjOj9DX1clVn74c/75mlJpymhY0s/afa1h02mkcTHaz4BNX0NnTwm8e/guW4iC/vBbCrUTjYdY/uBeARE+cXV11PGV4+d09v4bgM7S/sx7N8uCfUMXlM+fwnx/9ETOUJC/qoHd/hGU33czHr/soX3rxI7Q27+MnX71rpCi3BZH6eqgpxg6H6W3tRqxsgYSNiPaDz4aAB6O6DCOehtBeaufcQXT7TsRzK6BsHJxyGvR1w87N4FNkILLfAI+Kvb8ZKgpBs2BuOWgq7MjYKRZYu3Yd6opaVUrNV26j/ss/QwwkCG9Yx7t79oAhJ7ewbf70+98RD4epVpxovlyiqew9k4PPm35nF/kzqwjZFlqZG7MhKr/bHx26Th3212SEKjRIIm2AL3NukEkGzb9hpHk1nIVOEk1DKqLRHB76TWqMOJmiQlEwK0Yqdav8+9E32fjmm3QMCC6dHsAKRUCrpur074CIYSR2Uu7Xueqi+cQSEbp74zT1piiqLT7m/eE4GOnZP55B465O1ocGEKdM5JVnDnBAiaDl6TgrvaxY3sgfpq5Cc0dIGX4+et01nDXrALFfP0VjWwuuACTDoGgwZU4O88u9XHnGAp56cR17Byw8ZePoyynDUSFIpN3kF5YTLAuw+c2NxCwTVX2V3PE5+EI2kYPN9D5zANWpUDknn+bOHoQJml9BJGHz2q3cfN1tLP3YeQRrq2h4+S0cxSU4z7yFRXyKPfc+Sc9TTWApTAtW0N/YSlezjLQepg/rgG3gGR/AiAtC7+6UMRwnsMgHr0fB74bt+2BPG3Q3s/Mr/wMHeqEtBG29UOKTi3JQg54E2swAdmMC4dUhnoTmTCxkfIl0XByB7IY29n/t13hyvcRDCemLCI1klJ5Oadv5FBd7Q53vycRIhOO07qrHe3oZim5gdkQl+n5EZ5Aam6ZIqXykhmKjPo9hzllRi0R0pJ2V7DyGJI3GYdv+EadUVTpLR5PXofLNqxbxu6fWc/aCSr7w2fP55l2PEG1ZRbBmIsQ2Yeteeg2VO3/2Jng18twCW0BnfdfhNxyLssV4W8mHhB2+RxzY82Xxx0fPEpf8cKZQ8hGKVxXTvnuacM8MCpyI4EynOPvj40Vd8/Ni865/i4/eOVu4/EOoWqdLFV//0Vli7WtfENEDD4ivfvwUMW1qrphx8kTx9fu+KCovGC9cM/1i4U0LxDm3LBA/eOwzwlvlFNQ4BGfnC2WKWyieIUSv6kA4h6OENYkORlUELlUUXLlI+L75CZH30I9F4DOXi6LLzxKL/vRjgUsimEtmTRGFs2oPy30acT+XIgoumyq04mF5Pj4EkxWJIHc4BCXjBeMnCxZUC6b5BMOR425FUO0SeBHoirj2ue+K8jNmy996kdd69LHbP3TkClAFLqe47rt3CaffK887nIchnx0gLsQtJqEe455HOVRE8YXjBW6EY07uUA7TezmOMLaKqmTVD5xZXHeUw6Uq4vOnTRU1Qb/4zLICseLu88Wzv75e/OPLHxK2tVtY7X8SL39nqfDrCK9LE9eeXCl+tGy8mF+aI7JlkaydDb977kH6bIPnV27kRz97B9+EIiquL4W5gr3PryUtDDAh3JTm3ecP8MwbT/CHJx/mkfu2k0qJQ/ZmOmXzwotbWG8l+FdbI9sLndQdiOLye+nrjVGSV0J+Tj5V/kJqyytIRVJoTgfBCYWwtg/RlEQMiwXYxki3tWKrTFyyDL2gDFI2vc9tJLbtAAG9DNFr0f3mJna+vhIUuYR2bt9Lz856CheWUz2vatiNIKfUI13kk0qZe8tnUcaVwmVL4JJFcNo46FHlCmsJ8FVD1ITJxXBSyUjXqaJIz1sc3BW5kJtLz/4W+duiPBDqkB3i0qTuf9ibyRgjqTSP/uy3pGNSREy69eP4Jo8/1Gcys+cgScwxk6uyJBt6NzeDBca+8Nh5WsPG6kgOh0MdGoMWX30qTp/zyL8bHIPRv3dkEBaD5NSlSnwEMoXguY11tEairNnby3+Xb+WsD1/Fvl3b6G94G6VkHqJyEikTTFOQitvMXTyb6eNyj/JQY3f1mPRspIcr7/oR/9yxBfWyYkLjiqg5dSZKvoKdb2HviUlApwmKU6OzM0JexTic1V6YrMA0INOvXZtDPLytni899Rgrd9dhIdi0dgfPv/YavY4ooa5+Dgx0E85x8a/nX8NyQXhbLzhVCBzeZWOYCi2EzYGtaxEBv9SjDQtefIODt9xO9IkXwesg1lQPyWE6hgL5eiETxtUeOlVY5EaJm2CCUdfJis98G7NyHnzkS/DF78PUy8D2yIs9QTA1KKqEylroHoUhS9vQKTlLsXRW/uYp0q198rtwckjB1hQIeiGZQqvIRykYljtCBBCggEhl3MIOnek+H6ddeRmOgJuFF5yOqqpYyLBXJNuXewSyOixpzySOwAmDdCxGOgJteHYN6fhR4kOD8XhjVPtz58P1HwG/D1SFwivPIXfp4iP2wRKwP25x8qQCfvixU7jEF8PZvZbpM6p4+cFHAZPioIpbh7Rls+lgN1E37G/qwZnlc2VtI73z053gUXEvDVK6dAFi/AS6N7UjDqrgUUC1ZGTahnOun8ue/c1ctmgJM66Yxrb9+1Hqo5AHxhQdsz5JU0sY06uTaumQuCob+uq6KaqYQNo02dlax4BI0LGrm1RbHGKgLgmgDBhYPcMm6iAiXAHV7cROpLFiEWjITCMF8AmUCg9CC8LeVgn0HO45smDf+p3sMzO5KwjmLZtJ18E+tq5qgJSNiFko63Yh1n0bampknCPuAHcueCtAdUFBIUxbCv96YeQKPsyzlWjpIdHyNuga6Dr0D7MNLAHdURCCKdd8hN76A3Tu3gfdXTDQLyVbTTU0HJCpE4bJ83/6E6rbha7o1Iyfyhb9XYx0OjMwgiOKg2zJqYGqjFh4lGIPojc5lLsy+KzHgwpXwEyOHf86ROEjnLcFSmsP+rVXYD76JNfc/DlaD7bw7NoNkD5yWocidEotg4pp5ehiP+OM3fziuSRX3rGLgsJiirxOrEiaWixC6zdz912X8fBr27J6nKwZKfekIoovnMr+Dbvpa+qk+8X9iIM90AfqgIEegHQfCB0K5lZS6ivkO3f8gN7OCAY2+VV+8KtE0mmscpX+bW0ETq8iNSDkqlcOia4IBx/cBi4Fs8Bk/5pd0I5MDU9B7qLxhJ4cBnUJIOdLBPx5fqL9scNSLTwLy5j9P6eSqJhO3b+2kNjZDP1JCXkZjHQ7HbhuuwqxcTNWJI7oi7BjVxsf/epl7K37J8muJIpDRw0GMeu3QrgVPvN5SIVh72aYOB7sAATdUDsbPAHQw0OG9ViTS9UkDAZk4p4tpI5qC/DodLXXYeaXUv3lL9Pyp99jDfSD7kCZMBklmIu9cRsIgZVMYiWTGMB//vL3Q7fvxX6/LJQhRbpZQSYm2gqec6eSfHon9mi0gZBYOmFm0fL76dzGjQjAUED1+tnz6husf/rJozIRwLsN3fxljRt3fy/fP2ce85fkUfh8Hase+Dezzr0QUxFcM6uYu07yU/nRxRgRiP+jNasuZa3ahbb1s+cbK0nvG6C/uYvQq/XEtvRB1IYUVOYpFOVCboXC82+toC0cZtGSqZj9Anqgb0uU/vowBSX5uDQv9s4+FpZPIWdcnmxgADm4NuTPLKFq4ngK5haCC1wVLtxVHoJRQ8YxBmNzBrgqXaBDtDcqJ+EoXX7mRRfxxWt+yMBb+0k88xoIId3NlyyUT68Bs6rQJ87Acdk5WC3d2O0DdO7upNCq5oZP/Q96QRF2Tw/m7jUg4pCMQVcjKDFwuCHcDjdfCf48+N5f4EA3yvxi6aXzDFurvPmyBoPbD+k0mBlO8+aCyyPrIQBFc8fxtz//iptuOh9rxWNY27fL62wTpToX94eXQc7IRLXRdEKYSFdkVvmg+zluQVmA+Ir6w5kIUDQFzXuCyoBkQwJsYbLin38j2tAw8jv1cJ0sYdjc+9YBHt4R5aH/eZLWt1r5yEV+/nD/JurefZdw3KBdOHnBLOSqO55k7drXufKs7NIosmYksy2BaDNgexJnoR+9xIMIW6gFKhUVDv7wnen88bu1XHRVLkYiySPfeZoIaUpnBHDm6DJ+lISOV9pI7ImQ7kvw9k+fIrZ7QDaQ5lB8IVLXT6IAQqqJdkouvpMrWfT9D3P61dfgLssZikMYkG4w5G8VYHBu6YpUSRToDw+wZuta2v/2NIQyPtzmbti2b0h6tcYxGsKYRh7ocjLbaYunHnqZ8dMWUXLe+eDzDmGZHAps2iNBq4GZ0BmFPfUyzffNZ8E0EM0pKZ7PmAyDWbb5NeCvhtnzpFo3SN4g/rPOwn/BGah+B1fffC1L/GWckZNP16p3OMQWDgVl27vEf/QHsE4wLGcM8hR5KThjwshJ2d4PobGtL2EJzPAwqaBAYHIOnirP/04HdY1FX7kD3/y5h32l5DhQjhBMjdgQnVhB8XlnEtuj0NcS5ZNff4mQAdsbOykKuKkSDk77+I1cePdXsutK1p0OicxEtUm/24iSMLF7LegFs1rnvy+00HIwzfqBJKEKARMUlj+zBbelY5coMg4hkN4sDbAE0a4QDJo7FoeMVmMgRc+LdaiLfYiWNKGCBB19aTqeeY14y8BQnwToXh0jkZaVhiZqEuDZlITpxZBOsP/VV/jjurcwezLBxEzbRJIwvQi2dUP5ONJ76uGRtyESk4xiCNa++hYb3t2Ie2otTCgHSweHAKUP9mwBd77st65A607Y2gjCDSRkDAng7f2guNGmTsbOyUU7eTz2jndxzZ9KYlsdJNLQ2UB8dTfCBRWzpvI/13wGl6pjeEzUtCLHTQfSNtaWNkhbhy2BqqrKGhmjEQDvg8ywybJP3MEzyftILs9IxRRDSISjiT2Hhprv4+qrb6A0L4effuXnjAl1Go7asIV8tmxtLctGq29mgiuHHaO+8pUU4J8WpGPN7sOfC/jZy010FRZjCJ3TLp3A3Y80IoAC1aZpyxbWdyboTgfJdc/IoiPHwUgKUqKQBHpt7H4DSoBe6OmwWL4hTXNTAqGDd3IROaeW0WHXY3dq5C0ro/uhOul9SYBmg5qjYAwIqAS6QCuAcUuqEW4fB57dh2g2EbkWotHESoXYt+FJ2JvMBAHJJJhZGL1pqeqlgYM26AYkBUQg57rrCT/3NOb2LnAhJ4EXKA5AYwQ6YjJY2t6GvuRczLdeGFK3AFQQi2uIlZaAeyZUzoL7/gGpiKxVkOyRjhIBPPcoTLoQdKfMHgWJyvUX4BpfxR2//DWvPnoPO3a8Al09cu7MrCC55QAYFnZmle9X27j76b8wYV4JaUcaLU+HjszbJzPRMv8On3CK0yUnqnWErNP3QJYleP3Vt0iWF0K+DwYy0dVs0igMC7srwkv/fZnqSVUoDhVRmAttfSMuUwoKEb19aEV5aIpN+mBv9owkBGv+9ShjVaiJ7m0nqh653kJJsZs/3r8WBYVTZuuHkkdPOXMaotBDeOcmPA4NOXGOTVmrdtdf7iaQo0AERFscGkxIIOMMKcHBhgRCA5xgHIyR6yxEbbNJ10XovmcfxIfsF09NPiWn1qK4FPS0A9KQW1pIzoJqcq6eguPiEtBAbE9CVMC+mGSiwZV50LYByHNAiSYlQ0xI1IGqQF+M8B//A/W9Ers26BtNAnNmSMxarh/OPwPiccy3dkpuHu5DtcHe2yYj6BNqYd4SmHIGpBQ4q4LST5+P77qzYFwxatlk9KmzUa/8DASqZSe9KmqhTVWuwa3zapiYF4COPkTEJLmnGbrDjJgxKsQ6enn4r/fw7MrVtBkahiUkzGb0+zzkkFMAJ5a3AkvNsuRNljRz6QJ8E8qgoQHGcOQck4Sgo66BDSvepeiK8zgsxdXlYuZ3f4pWXI3V3C2ZiONsI9POmGQfmRtbu1LYNli2YOWWzkOX7m/t5i+PbSGUErQ09iPs7BIKs2ak9W0GSVPI1WhP5u/weaBA7RWzcE0KYlS42X/3akQkLSd4Gnl9Bv0Sreun7a0m0BQcBU4QoPm8nPzxW/BWTIGYKhllrOzJJNJG0i2ocsK0oBx4H0PqYY0GwSSoSQg6ZBxnUEroKlROhGXLYP4cuOFOCNaAWgBnfQImzhz54Ae7pUoXTsCBJigPwoRa3IuW0Pn8GmL1e1ACHhwTZ2C+/Rx2JAKqE7BxFfspvWYxLbv28KE7L+Klvz2EMIOHbLxkUw8Yw2ZN5l+j26Txhd0EjHIcwiNhNsNMD318UcZND6CBqwb6mmXS3SFSyA1OYmjlOX5qam6i64WXIHJAju9k9ajxIs2vjNmUAmjtndA2Ki08nabhoUexwr2gaLh9he+pn++FkqmxufX1jZ30Rg0iKZs/fPUvRFf/Mqv7ZT3CjTELc9CBkdEuCmeqcs4ACOgK9UONB5pjMMuPiI9cbRm8Ni2wQwYiapPYJdWFnrUtPHL9t9nwib9iZhDPzkoHjvwj4NhzfOT++g4YsCBmSTUz0w8OmhDTyP3t51B8OZnrM42rAt5YC+6AxMF96zvQ1YA6vRoKc8A/zDAenDTNHfCfx+Afv4ONL0BbO2pDP+6FU6AtjNjdQmrDRhh3IewwwBgHeh7pmEXv7lZyx+VRXlOGoaRhICQDy2NNSF0FTcUxczZqH7Rs65EqNEjnhK6BphIsKoNMeTLFoaAVJYEUiGFwCsXB9378c37886f4xM1/RVWP7uUbi8J1bST3NMrcpBTQcHRR4cpRx1TJzESK9pWbDpccmka8vQ4SETSHj/JpZx93H08EacOcKbk6nJUvzeSzl9XS1Jpd+kbWjORaWoI6vM5YLoTCAk9J5haFYNQoGOEw7Ephvtor1bJBsjmqbi3SNuH1LZjbB7A3hqRHrt/ACFljXu+eUcyd591M7Y0XQrECgxhHDSkBAwHCz7yLqO9Cr5lA7h0fl0lytgZBP4pQUIvKYN16SPRiv/ZveP1haM3EDZzA4MIRGoCmRti8EZIhcBZQXnoa4+ZdDQmHjLNE66FhDZxyDZzyMVh4HaLPIPX2XgInzSF/9mksvvvbqMEA2mWTIU8//A2ooMwtp+SSRfTsq+eZL3yDRN+A/E5RwOEAy8btCqL4ZNjfkRMgePaCjEdxWNBUGIQHWvnQVWfS2tVyRO3nmJQE+kEpz5HMm69A4diLW7zNGgPOM8aFg/PW7aLgtk+A34/mcKMeKc//aDSGm/t4yekcep60BecHFT45Qeef/9nLH36xJrtuZNvYjMvOo3BJBbhACSos/fqpfPiTF1E2WFchDolXm9GEQuHiSokIHr2ADX4eHNzh78MBwcuqUSY6UKb60OcXQpAhlzYMvQAdXFNL6O9tJar1gk+HQTR9HjAhgHLLFYiX14FpYTYcJPzoSxLvlrRgwz54dwNafsXQvXv3QXe9LD4y2Lex4nv7WyG3gvr1YfY9vR1KZkJwghzK0GrYcy++6TlUfPFj4PJD1OCg7mDrtlb8pTPRJk1j5imX4iuvRAn4cUytBY9DOk9MgRaJkNu8H+IpLMOE0lLZbsqAhFTdWt5ZiUhI97eVSCAGRhrViqIQ8Pl56tkn6B5IcvunrsLvnzbWaz2MXKNnRBioKsDz+ctR8r3omgfvSdmlFqCAI+fwApuH3n80Rs+PfwzRKEUV4wh17Bp9h2OSc+Fk1ELfcf9uOCUyCAuHIjXYnQMK4YjNyTkpntuSTarwcTDSji1b6I60o890oFY5KJs0CX9xMfGYjupS5YTvB2VDmsk15WiOo6QWKuCY7mXuT85FL3UeOhdq7URMsGCKDmVOyRzlDA28SiatAdyTa/nX/b+j46UN0GoPrYQpoFBFnxjkEOcKJYNPE1DiQ/3kTeixNHpPGrVokJlsCR1KRg59HJk6oIGjBmXK6dCxH3a+jljzBJpl454zR9a/dWiw9d8o+18k8vJ60AMwfjyB/ApqT1pCoabjUBzEe9KkuqOIeAJjfzMkDEhZYAvMAZNdv3g6Ayuy5H3HIpcLnC6sZIK+594eKQkUlUgywbYNq1j52guce/YM7rz9V2jqsUGYY5kO4y89G787BxFOYvYlSWw6cgmsESTA6B0lZWxgeCw3JTNsi8ty6O+qz+6+wyi9sQ677/grDI1FhoB+AXtTNv7aHOZcXENPlpI8a/d3srMf4XNjrg6j57nZtW4/TSu3k24IM/n8aRRMKmfLI+8Sa4+w5rH1iPQYPVCRwNUIWLUuDkTasIa5c4XDBLeO6I5j9kv1jgOMxHI5AVUl5XMS2dKAtugUbGMLoveAvC4CFDox7r7n0AqOkYZInwSFCpOimZUUz/wKux98GgJB6B6EgWQaCvpgUj7UtUB4ULJ6wSjFXfZFjOSfMQ+8AoB1cBd2RwOKM4/Zn/sJexo2EzUNeO5vUDkNJlXS++t/8Ubxs4hiD/amg6QiSUikZNbnMJ8BAkjJDF6Q/9N9hEkbjx3RWyUyKlLA4eSZv/6ampw8Nq1Zg20fOYhbo0GjNbbDLPHqOiL/CUlkAyB6snOr5U0rwMImvLv/6BcKweaVK8Z8HsWlI9SMrz+z2Iwg05a2JRzVS5ctlWuwIQYzUir5lnriQatel45nRi3dr2zCbE6y76G1OMZ5sVI2Bzs66RRxEiH5ooQiMoHPMVrzAiEQdTHCexoRfZmLDMj3FzLhsjls+vUKREPm2uHARRP5pp2C8GMrsbs74LUeOBiVDJZCTsbWFBxI4ltQixYxCO88ADUl4FNhbxudzy2np+Yk/FoM58JpdHc1yIyweMatWFYOp5wOex4Ejy0BqkIHZpFYsQHUy0FsAPogHkKYgOqhbdNadE+K1M7tkFcGO1ZAYwkkkpiVpShpHfJySOcV45p1EubBDiioRNn1NnNuvow9T7xCsncUaiBtSvto9CQ7htGzbMosLsnJp2F/I3d99nq67RRijLI4ClDugFsr4btNY8/Fzp0HjtrWkeI+4f0DcvHKho7wPMWnT6PmzEXsPWk8fT+8D7G6aai9wcDwtBzoMqBzdBbh8dO4aTnMKcrnyXeaSBnmmBJ6LMqakYwNYaxIv4wdAVZ7ChG3QRUk68LEN/aAD5RiBRUNR7Gb5PYMmsCBZKwkElOngBoGtVrD2jfUhlmXIrcvB5fDQ3I0vD4TCnJMDaKM95J+rR7muKAxCqMXvJkTocDEaukBS77I3LISKi85iz3PrcB32YUk6hoIvbscXA78115M4mAb1s46WLIU1u0BRxCq8qXq4QrC7hSIhcA8sLuBz4J7AMynIN0E9NP9+v2gOQEVPB0yfdxKgLMS9nQjcnIhGmbRRZfR0h5m06+/D50HwKnRHYtgxsbIjlVVgkvnM7B2Mxgm/ilFpDojGANHDrwWuD18b+n57Fz+Jm/2HeTW6mKMKXPp9OXxwFMvIjKzXlXg4nHwxVrY1zYE5j5u8quQsOXEHvbaDmkbx0maz0HO7Ar61zTRuWInrrYU4cffQDS2y0VF12XuzOAkP0qF3OOl+u40H7uighXvHuTtDiP7ilzZZshykT+7LElFHopbEVQjKBqVJTlOE0zJ7CLnZuQ9fQhlgfvQvkXD76tVasJ3Zr6oufciMf2Vzwil0iHIR2awegYzKTPXF3kFRZn9ljR5TvW7RfkFS4VaUiTcn/+SKHnorwK33J9HuXiJoCRfsHSqYMFMgd8vKPQJvKq878IJgrx5gsAGAT1CatNpAb0CrhOZLMHMoQlUXeAPCnSnvJfDJXDniZxbvi3IKRKn3P+SyLv5V4JcuacRqiqoKRY4M5m/OiMzbLVhma6qcuRs3sxR5naLH5XViI+qbuEB8au5M8Rnr75atNY1ijk1MwUg/A5F3HlGnmj+mlN0fwTxs+nvPQM1b/E4oeWMvdfReznypxWL2uvmjTinFOYJJeARmq6JQFHeCWtr9OHy6GL6vAqhDBvjrPgjW0ZSbq8YmqjHcwwyijKMOYb/zR/2Qs6vFNO+fbbAr4ycSApCLVNFyQ9mCu3HE4T7ucvFWf/9vJh4y5lCOaVYMEkZ2jjMM6r93FHMWlApOOliwZwFcntHEJQE5f9LSwV+VRD0CPy6IN8tUBCuc6cKJZgr4CsCDohMzkPmb4uACwWq3PxL87vE1CtOF3pFvkBXBfMmCef4EqHnBoTiCQpUTZTe8m2hFk4VBKpHTpaAUyjzxgn1zMnCce08gftY6edjHxqIGSCKkGnnv6quFp+dMUcke/vE+uWrxCfOOVU8+9UzRfLv04T9A1WYn1HFZ6veRzr3MRj7RB+KmllY3KrAc4SNxE7gkQ1l7bUTu0Jju4OPRYPiVwfKFcjLNOkEchToQ6oEJaAvyiP3ynkoF7lhduZ8roI61cW0n55F/6oOrHd6MP77Lql2A3dhGYiEjMkYsq3AlbUouRmNVQHyNOnXzJByzqmgR2D3NrkJnQ50DshtUiK6rPAzkJCG9V1XwNRiJk2ZTsGEKqAH6S0Zup/T4WPO4j/gqbwSZ54ff6Wfv/zox5w3ZzrX3Hg+t123lO//4ydc+Y3PQDoCtkXHP3+E3bNHIgYypPrdENQRoQT29g6MTS3SLV4zDgqCh4/rcNNDU4YMbqRpui/TWxNo7emnsa2Z+o3rMHtb+e1D93PJLVfgcmsoPjeW5mB/8jhrzw0nkeV17yXkk4GdjWguU+HkpPmncsGHP4HqGDtoqqrg9b83VMfxUtY2kj61CLMpCc3HUfhZg/J5JXR09iDmeVCmuxB74ogXMjXedAVUgT7Zj+VK03+gmZaXduAoyifd1ibrfczVsYVJ+9pW/PNq6btnLVZ1hNVv/wkMJ5xaDGvSYIPi0Djzs59lRcMvia5plm0MDIMHAWL1K5B0gOaAEj/k+mT5q5Ia2LluKK3ZFvDSGii12Xnv0xlb60YyRRoAk6ICk+9/I5dFi9xs334H1XNuo6e3gSW1C/jnff/E6VZxeQL86o/f4fy5p/J8gZ8ZZ80n3tRNe0uKcFcjpmGAQ+fsb3+ZflWw6eUXEGt2Qm8YHBrO6xdhrNyE6B0YObaZbmpBL47yXJK72kd8PXzN25cIk59SefZv9/Hwa68w9aSZXHXpUq6dNAM1bbKrt421fUdKR82eipH+nlEIwkPkLHKS7klnh6UbdChYwz+MpLBh0bpq1JYzw8i2IR49cWj4o1K2qh0+RSgVzmPbSW7kbt2KrBJzyfcvFxX3LhU5/1gmCp+9Spy14ssi79TMDtROBLNUUfnAxSLnscsFC11SPfNlVLRaRQS/Pkd4zy8VVKnyOx1BHtL2ObVE8O3TBYN7zGqqmPLgHUKfUzjUn4yNJG0LRbBklmD2IoHuEZy/UPDxSwTBXIHHM1Ll9CLwKoIlOUINOsSnf/xVcc7ZPxaa+ryAVlGQ3yz+db8hLOvo42bblogMdImnn/qduOmW08TmXW+JPfWbxM9++z3xte99Tlx05VnC5XEId1FQeKePF5QGBSdNkf3QNbHoG18RvmnThJqXc+QxH1WNRxn1/RJFER9xukWt5hSTSyrFT775E+F1OcWjd14g4vefK748yS38J0AFUkH4QRSA0EFUI9XM0ddp/iNUN3LK6k+Tr/6QUBcvFjg98nye87jVx0Ch6+i/Uci6MlJW/JE1I6mIouvGi8CMvMOdBJmOafN9YtzPTxOL/3yxUIs0ed1pHuH5z6midOU1wvnjqcI9u0g6CjxIO2iyU+S9cp1Y8O43hfPkgpGTHwQBXShBx9DDk5nkTgRn+QU/O01Q5h663q9LZoOhv4oiOHWJ4IKrBCUl0rgHkfO5y4XjC1cLrrpQoA+zRyZqgh/PE9y2UPDh04R3SpHY0bhdhNti4qu3vytKCr8vXn0xfUwmGsZNwjKTIhFrF8lol0gl+kR7yxox0L1LxONd4jtf/bCYPbdClJ83QeTeebbA6xg2rorA5RaFl14gnFUl2U2kUe9mhq6Jcx0OoYBwaLpYNGWOAEWMy/WILy+tFgt1hO8EMNLgUQKiGMRiEOcq0k4b3jdfjXxfzly3ULRhi4BfEWiKmHXjR8W0ux8XgYu+LEAXOANyw+XMdU63Qyw4fbGYMGP80JxQ5fxQHYpw5KtCdWffX5d7iLEdiiLc2siFKRvKXoG0ofupA8w5bS6+Ev/h4nkGVFx7CnMv+wiE3YioAA3Ui4sR+fn0tYQQsQDJrhAiX0W5ohhtiQutWMHdFiWYW0TB+fMkoHO4zzFiIgYMqSsvViEHiTgwgFVReHQ32MMeI2oOYfoGA7gOFRbPhbZ66O6WMt8F/sU+bJpg3xawMj/KBU6bgGP+mdDUhSoMzvrR7VSWVxEo9XDH7bk89fBHWXaeTpbVbEFRUDUXup7Hw3/+FZZhUFpxMrmF03C7C/nOj/7G2yvX8vZDL/LEHT9kYnX5sMqlApc/yOmXXo+noDTr9obTHtPiHcNAAIZlsmXvVkBwMJTg7tUHjnfjhWNSFxLnug5ZzWjW8C9tiO2Xrvt0PDVUGhokNtMSbP/Pf+lc/wqpCp8MeehOysrHH7rMMEx2bN1Oc53c9A2QRkqlCxuBEbaxjyMtSyhDL9IUgtR7iQNkLZFgpCdt9ApYgFBOcgnfVxeJ/K/Ok9eOU0XRQ+eL0g1fEurv5gr1zGIpJWp0oV9bLS547DPixtXfFYGbpojc/9wsil7+hnBMzj/y6uFCSrnhInt6mcDvEkrANXIld2b6q4+6x6Dn0Y1QfnOK4PsXCCYOUwWLXYJrFwhqiwSz8sX4r10i9pghYdu2EEKIVE+dsIxkFkLIPnQIIX+7Z/tmcf7ZZwjDSB/ld5Z486X/is/csFgEc9xCVaW64ywuPPK4HOfhGPW5FIT3BEqk4YcKojyba/2ZeeRQBLm6dPk7pUtd1XTh9Y6h2o6hulWfVCXU91lQcvSRFX9kdZUQUp0KHqGxAgSzEUxXBEGHPDQEH88RfLtQ8N1FgjN9ghpdUIrArQjnKWXCVeEXxRdNEs7PTBbM1wVn+QQ+RVBbIl3HY7XlZqQbXpNxFd+8ccJ7Ro08F9QEHmWkijj68CK4oVzk/PYW4Zg+Yej8lICgJHDIXX/yDeeIqJ0aPtUPMcaRyRa2nRbpeKswU+FD1z96/33ioxefK0zTGOM+Q59tMy2M8H7x9kt/Ew/e/2tRUVEoNI8z6xfvzBzZXj/apvp/cqgIJuQJfn+z0Fb+QzCl7Og2joagSh+hxioKoqgyeML7lg1l7bXLWZxD+K2xPTtKkZtxN86h57UGYrsyaNkiRaZ8h024f73MXh0sHiIEWp9BciBO10t10s3tRe6Lk+OUYrrACZ3D5POg42bw1OAWQpaQmaV7D8K4CigMyO0ZB5JDkNxiP0wqh33Ncmv7PlNG4F/oJFpgYA9CgxzA5CAsbxtCKZclQBigOId1JBvS0d1FDGYoCmHz1huvcMbpcyDeCW4nwjRA96FoHkSyD3QniiMXRdPRAxM49YLbOMUWjKus5p57/sRA3OC1V9eQPgZiQGMo+z4bEiC3knF7URYsQpRWw3OPQ6Q/yzu8T1IUmDUFppZD1ULczipicWtsaNSh38D0T12MaOpm972rQchLu9sHjq++3gmirDca+807P+Obl3+PRN8YMJaAgh5w4qzxEX+nb+ghXMBlxTJVfOsRcFCDIYDyAOS4oNcYKhwySCoyVSI6bOMpJ3KmDNZBrHBCxSRo7ICuXlAV1JogdmsMPn8TnHoWfOsHcKATegfAFQAjCdVToHmvrMGQg8TNDAg5Gz1Q9fGT2fCj5ykOFGQzTEekSF83t15xHt/62GJcSh/eYBCHoeFUc8ipqiLZcoBIIkb+3FPRy+ei5dWSyS1FALaZJGUY/PaXP+DxJx4lbRg0NPWTSB4e3NOAaiTeNyvbx+1myh//QPvSs4mPK8XWXdif+RXc981s7/D+yO3Gf+ONRHftgN27wbJkpaIjMNK0U6aSHi8oWDKJnfeuJb6rRzLS6Iqsw0hRlDE3DCsozyEVM4mGjowgz4ZFspZIrzz6AkbiCBHZiMD0pzFtW2aqDqbHaCqs7QW3Bn5FzgtLk7tiD5INTAhI4OmBIxTZFUiGG/48GnKj3mqXjG1VL4COLoiHwe+F8hK5J5Eeg2gzRPdBPAKlOZKRUpm2GndxyHNiakOp3xZgK0Tz3UTSYYo5NiMJIQh3d9Lb1cqEGXNRULHMCLH+Zh5/8D94wm3Et75M50ArIUOh0O9CCdmU5nlRNIuBSIrmd56idNpsyhafi22Ds3QSlC1Gc5fgdXj42rd+xue+8CVMy2TLxk38+8G/8tCjr5FID3l/LKCN45Cdc+bgveFGYk4XVqMF/TbsSx3HHd4nJZNEn3wBiqog7pJFZWCIiUZJmLLxVZx22xlU5Vby2e8sZ87VZ6D4BZv//taI26pOB4qi4PF6ueW2W7j3D38mkRgpCHrb3n/8DI6Hkf70zpHFZY6C+5JKkns7pMtmkOblw8WlcnV5vRldlJB/xXl0ff8hCX9Xkcf+yOH3Hj54AqmOgYzgW7LEsee0amxXLqnuNthwAOxeEKb0wFVUYrf1QKQDNu6WKNmmdihySJBl1EZu9uQBkQHXBoOSEQcyC4YqWHL6Qopzs0xkAxLxOCKVABRC3ft44/E/c+89T1K3r5NPLlTw9Hbj6RY4+8HjMrBTEO+MozrkupMWcer2dhHeth5LhbzSSgrnzMO18COo5YtRVS++QBkoCmcsq2LJqWezePGv+fpPfk8oZpKOK6hGhJnAOE3jWcs6ZtEfu6Wdjv2d2J5CKHbLLNiCGO9r1+Xjpd42cBSBrwSSo5LpRs2NVa+uxVNVzkqxkmQqya63N6L6VZlh7HEiMsUr7Uzl1crJk7nuskt5+O/3HsZIJ4qyr2t3NOmWAu9+E4cjh4jae6gWN5t75aQvtKCmmELPNC666iYeuPsJrP7oUE3swXsP5uQMq5UNmXvlA3HwnjeZ1IZmrP4kJ9/1P2y773lSRVE4aRG8vRLaWmUZrIXzYNN2aDsgk+AamqBG4LztdBztKrFfviq3MFfdoDjACoGhDzERgKZzdeEyAnp2GZiKolA6fgKpgIoZ62fg4C7++c/neH1rCzkaVHkV3CFBgQLlfikUIwPyuQxdbgfrFBDpsYmKfkI2tO3qZ2LzbsSaFZQuPB3NX4Nv0YdQ8saj6EGc7gA3f+pbLD3vclZt3snn7/wG0a4IHUC/ZWfFCnbrQbquuRaRcMKieRDrhjdeyuqZTxQFg/lEundjWXAs6EOqO8wLP3uA6ounUbaohtYVdVJj8ao4FpeSXtE8Ih/Epaq88sIzDISjR77pWKSA4skOOnVC6suqDhWHy0myJTakUuvILein1kJ3MzTECC4I0r1uDVZ4mL00nGFsoMAL/fGRqrlApm/YEH9lr8TDlefQOzGXgfpdcCAEuRqU6hB1ZGo2pGFROUy8EHasg85usFWqZp2G09nPbl6V9672g+WBAwPgjIFLQSnUEa0Gbl+ASYXjsh4HIQSmmSYSH2DvulfZsmkzwYJcgi6NGsuiv0ngnix3v9RNWd/PBfSHZKWqVBrSYYmMMvozMDMNmreaJMxmWjf9G6ffQeDlByict5Si02/BWT0fxeFjau0cqstreefpJ3j40VZa7OEr1LHJ2rVW/tP4dta/OZEUDg1gi+Pr88HX61AGs6a9QNwm/cbBw67bunUbO3fswD7OxD/N7WDu5adkde0JYSQ7atH50qjkLzey+P2z61HL8/Bedw57/vAie1e+fuTEFwEudw5qSQ6JtlHF/VLIWTdYmajcxb6evVitYXCbsK0OSv0wuQpinbD/Ffyf+TzJfht7dgH2n++HiM3+W3+JErWGpGaFATs7pV7VEUULejjvBx/nzW8/TO3smczNH5/pm8A0THq6e1B8OgU5+WiKijIq+Fm3ZTMPPfBrdjftxdadeH0GmibIT8OB/fBqF4wvg9oyKMwFwwRfDhgapOPS35KMgykLKeHyQnefLBhvhgToabr3HaBpdTO+196k6rzTqV5yPm53Oe5gOT/9yVcp9Si072jinT117A+P7eRxBRxYaYGZ+j9U345CtrDRlTHLtx+RRNqUZpSOrJt+lN0OzfdQgTY3J4dTJp2U1bX/exXPI0CXItUq04IBE3qSiL6jh5zTHd1jL0oWI2soqJDs7ZMohXFuGFBh2WlwYAd4NfhvI1HnH8ESaDWToU+THkEjKgtZgmynoUPWvcvNgf4wVn+StX98DSOeYvqUObidsvqKbdvs3bmV1o797OmoY9kZFzJtwhyUYUNoWwYdB/fxxrtr2bilGWFDrl8lN20TtqHNAPozpfkEFDllBTCHW26HE8yRdrbfDYoBXif09IBLgGLK/pppCPjBDNkMDLTTuO+/TFy1nCnT55MIVOCaczpf/8bn6d3byWUf+xQQQwNOnVDKxtYeomnJOOlBh88Ye7r+v6LzpnhJxAzeajeGF0QaSV7AVGVpspQpF2UT6Dzxto8mINkVOvaF/G8yEoBw4rphGYHySvpfWIOsqu6CozCTMLN0t3bFoatNVhran5Tq3NMroVKD+XmwMYo+cS7mhnVYb66Wm38N3nr44tSWOWln8tQV6Nu8L6Mfy2hfU1s3Hfv389yT93DBhfMoRTCheALqsMqhtmWwbf3b3PvIw+yq62CwstRAxCaGXFeig5pLSEqgRJ6sDhZwDpX9c+eAiCN32DDA6YBoDIQNiiozK5xWxokZg3DUptfqZmPj6xyIaexNPcX86TlEW1Ps7x8A5GMHCwPMTsXZ1hYmBojRexv9f4Am56aZOk2w8pmjXJSCQ4b0e07pleTOdVI5sZT6zYergwDdXb38/a/38be/3HvMe/3vMZICDKRIPfA2xff9ht7u16CoAObPhVfeHFlj+1ikKpnds4cNXHsM2g/KPZKKgVbAjELCDf19oIDVmIRVYegbONzmGu7YALkpMmSqtSrkzJtFXXcPoXiM3/7x93zyqg/x7R/8CpfPTzIeZ6C3i4Ytmxjo7aGzJ0JLVzv3/eOf7G1qJj3q2Sxkjf8oMiveNKGvVzKRrktB63WAkoZcBTw+WVM+neFtKw1GSqp8CmC65HkT6ZxId0C4xySqmHR3pVgVilBZWYhlCTRVYf6MCcxbdiFXnH8eX/7aN3h17baRY/H/Eaq2TC6apVD+IjQfScBYIAflvce3VBWC+V6icQOn04HTrZFOvr942XtnpLGKm4CMI4WBhbPBX4ySFjT/4m9yl4i+iEQXHG98otAj41CtMSl5BHJl2tANs/1wIA45qgyytkZgXQwiNmJnCwinvLbULdEOAElQZ+ej+52k3x5liymAQ2XqjTfQ8vwrvL17B109B7j9EzdSPm48qjeX3tZGNu/YQyqZxDRNDOPoLmYbuYtjCZKZBoAiG3xRyM2V16guqeYnBXK7TQOEV1Zmdgi5eQZO+QhpE1Ipub54PGCl5LxKpcFpQ9NBg/qudrwKODwaX/n8TVxw7f+ga26+e9dd7L3tMxwYOELM7v8RacD4PChDUJMPze2jLlAVFJRDSX1HI1VVyM/30tMztn2oqgqV5YXs2NmMGTUOMVHA4ySSSKMoCiUVBXS09mS90GSP/h5ei1uD4AW+wzIX8StwUgBOr4R4HLwq4u23Yc9+uPFjUDMR2trG3sP9KKQUF6Gcu0Ayw2B9JAE0pFEuuBwltwqabLjsAphcA/2qLLi4bjt0deGcOZ6FX7wN3e87BDGyd/WT3tpzOE8LIKCz6YWHaXv7LW6+5koqp5Rw959+ydyZ4ygQjUwrTXHODD8nFeosq7A5twyW5cGyIMzRoQopKIeXtI8wJAQ1BSyHNBsPhqE3Lct7RzVZMDaiguWChALegGQwlx9UL+heuddaSIE+G3qTMsKQFrLWSkCHAQV2x+XWr30xk69+824+d8u1/PzrX+Dv/3qUadUV/H+NJqiwyC0n+cy8wxfaSaedRPW87Ipc2rY4IhOB3HB527aDuJwu5kyZcui8hHdK72tHS/ZMBMcjkUakNkNoa/xwiaSokFAhloQtbbLaoFOHWbPh1ZdhznS5TWTdfti3j2xJ7DoIvZ0w3gd9aWkcl+ZAYwwe2CDLaGkC8oKQXwHJOvC45b62XsG40+Zz1w2f5dZ7niQ6OMBV+XCg71Abnup8ku1hhGGSN34ckTV7oMxF2pniwmVXsWDOYhacfB6J0EGMeAtOdylmIkKy7U3qH/0r21/ejxoDdzG83QR1aZm9sdKWvGshu+1RwaNJ9IvilKinlAv6FCm1XEDUko4Gly7XKm+BNAe6BqRAjoHMmFPkEEczj6Q5ASH3n45xaM8CmjrC/Pept7CUt4mZ9qFX6XLp2LZNXp6HaNQgfrSNkf8XyQV8vRhKBSgpyCsHRhVdrX97c1ZQneOhZDLF8y8OoSH6Y+/d65I9I6lIpVwBZaKbwPlVhP9Uh1KmIWI2qkPHc1I5saYwiLSszbCjAWbPAl85LH9W4tl8AUYUe8+ClIAPoXtgTXem2qoGp54Mmoooz4EnX4ZzJ0kdavZMeG2dBLDOLobH3qT9tXXc1f8pou3DYBdNA6DpFJ27iAmlBax/4RWEsMHtYPaHL2Ljrx4gqhmMKyhlzsQp0s2tOPDm1UBezaHb+CtOIq/mDIqqvkXD48txRE0W58qKXQ4P7IrL8ig9QKkiGcrrlNGBdApiOgy4IalAwoKAAsIhQ2VEZa3KtCk3xPD55XnLgoiATgO8qrSr3JbcGhdFru5JW8KEksiFNTws0CmQweOzT1lMUdDPVddcyYOPPskTT78yYtzzFNlenCFIY6FsYgSA5f2QBtzuhuvzQHWAUARjxUCzUemOl7yaQix5YhaP7BkpDThAqXEi+tPEnjoAJrIqaFzgme7n3G9/kWc//xPsuZXw7kHo65GSJxiAabWweRv0Z1dLeZDUabNx+TSMdASzs1ta7RELnn8LxuXDyqicaR4nPLYCbv0E5Hihfi/0HQALYvXNxFrapMQcpExcoX/dDhK6huhNySL7ps1bP/47hBLQDRffcD55geAR+6coKnr+Amo+8wBlk77Ozj88jNpukKtCwilvg4AKYJ+Qm2GcbILulhuF24oU4gkLbB3iBrgc0rPr8UmghRDg9cu63L44OHzSyaAnIJkcclgounRMFAC1yBzFOJKZDjLK3yIEryxfTXlpJaefcz2nnHQBmzYfwFRTuITB1WdOJdYZZvlL69gDh5L/Qpw4R58GXAt83wGePKT4NSExFgDhUKLj8bejqBnY3qjfxswTx5zZM1IAuMRNwfzJ9P2lDqs5IX/dJzIdNFn9i99i7+2GU6fBVRfBMxth+x6oa0L7yGVYW7Yefw/NOIn9zeDKqHTVPglGjSdhb5ts263B6v2Q9kGuR0Y1Wy3oicE4FVpsWTx/+FRSANvGHAgR1R1okyejVhVhLF+Vmf0wbfFUPn/HZw8Luh5GioLiKMF7zidR1uxlz9p36VIFO0Nyv1KAqCrrWb6bgBZFSqQU0q0dSkPAK+0oj0e6vR0WqHEocoNHke5yhxcCBeCwIScGBaa0lxIKpDQJMXIZ4BZQiowMOHVoMaW3cGBUt20ELR3NfOrO2ygKzuHyD3+FGafM5NQZBcyZOY6md9/k0RUXYiXNQyP3ftdvBQmyLwWu8MPX8iAnFzm/0hIqGRnDD6K4dUTiPQaPjw8wMUQq5BZ5s7o06zQK703FVF48nfofrELBiY6DdHMMIqZkKC2jB4CsDlRaDBPmw87tYISgwA9Nbcf3IAowJR+ISD0mpsDUIOzqH8JT6pnrDKDKDdXVsLZObn9y21KsVBTjwW1yGVVVKCmG9lGeugkL0cZXIOo2EPQ5KK+tJtLVxiMPPMjJUxcdm5EAEGAnsNqeY/1Pfsgf/rqTZ4X00oG0jU52w+oEeF2wzA+RBDh0qJkKvkzRBEuREkZJgJaQ+6TlAO4kFHmABLhtOcRmDHoi0GfJGFUyAbG09PgZmbYHa9avAJqP+QwOXO5Crjv/Ku79z89J9W1j4eKL2d3Sd8xfHosUpBPmBuASYIID8meB04t8wMymJtY4uOZReGp4Z3UV95xikhuPvJXlkcjt81NUNY7mPUNGl6YeUkiyohOaRqGqNmqTCUEF8l3kFJXQ192IHTFl1DDoHcojMgX0DEBNDAp0aIpBU5ghTTvL5UFToLIMNoSkda4B7YmRusUkJ7QaUmfKsWHzPjmLVAWjP4loGJCzNWVKb6Exupa2Cq3NWM2NYIWY8+1vUOxTuGzx/ONgIrkJctO2zdStXse6LT3Ui6EcRHkB9FpQrMsgfNoFmim3PIqGJCOlFLmpupGW2qppyaE0hMxzjAhZok9zSgGbUyglW1EaPCkwXBIkn/RA0pD3iAk5R4vJhpEMUsl2Xln+IHV/tsnf9zzztDCHb2d8/JQLXAN8HJiggRpEMpAL+RBeICEn+MHRYs+03xMTASRjUdoOHKR06qkY0TZ6WxrI0zVCaSsrQEeWrz97Roo92UvqugGqi2poen4PPVa//KK6QFrEW0dFhwVgpqCvT84I4IjatacIkgMyE3Ww916PDJa8vjNzjSINiv5RddEa0kPuqV3pIZ+zLbAe2wQzqiS4bW9mGvWM3t1BAdMNdgtg8ebf7iE3z88vb/101kxkmWn+9ftf8fUf/pzOUPgQjw5fLnSktKj1SKhQXRymODI15k0ZpFU9ciIl09J+8rqllik0KYEsEwq9GVUuCUkdrAC4otIJkNblhg3oEE1DwszU4DekKzxb6oiE+cHv/slNwRRT/DoK5vuK22rADOAkJIhfHQSaDm58kIsEJQsZJ+sdK0XIidTMR/upNIY2lwPJlKOAM8K2sO0kqWgIVVGpKCgi3duLkUmz0JQjgyQmTM4uhSb7OFIEwnt6KFhYjKIN+1lPBJqG+XAG556RBiMm9wyqrgbH6KDTMEr2DTHRIJUVwqevh6J8+TkhZBp5xagEu9ER8BkF4B7GALtaYF/Lkdv254LSJt+ux4vz4g+h5RWhaFmuMUKwc90avvy9n9A+EJbFjDlc5urIYGtAgWo3tNng9WUmu51J9nWCqcv/FTWjKbuhPyXTLKIahHVpS3XGpf0l3OAvAdMPig/cuRIx4XSB3wUdNrwj4Hh3HmpIKPxmr2D5nvePYctDMlEFmXXOzpw05fORwRGiQEME2gYZYbj3TlfGFg8WIw23MdBndipB174NRAd6cWgakyfXojuHqrMWeo688V/Dvuz8k1kzkhpQsZJpdvxx3dD+PZoCHzsbLlssGag4AItqJGjUqcHFM6SNM3kC6EfZi3O0O9yh4//YjeR/4lMQyIT+FUWqjgeP4fVLMAqEKYZlWiqQXwm6H7RMjlEqIi1cOw2JGKknHsewLHa2tmShG8t9Hd5Z/jLdkWNvKeJWoCUO41W57a2aKz1wtguSGqTVjERR5TokMiul0wcDaYiZMu4UtqWrWAU0D/SpoJdD0k8G1CnLVrSHYYMpyxe7laG0LoWhhfxItD+SYJVh87b1/lFE45GMNA2pWJCDdC26kJImI5WFC+r7hiHBRkzuLEwCZdRvdA7bejNlGjy5chX9sSEEdE/8KFsrnXBkgwVTTp1F8Unj5NvQkG/5xZXQv1t22OOEa86HIp9U50p9Mut0+UpIZL93jbuiiJuWLuUr+eWoHj8Ex6GftAC9ZsLQgw0OWGVQoj5BfrerV/Yrl6HZ4lGg2INaVkj5F++Q8GpbLmNaQT65d9wu7TwA1Um48SD/euA+rCMykkAIG6t9B6RDtNTtOeYzuVTIcUC3kJLCBrqFAvmQcsrYsdAyGo4hk4ojGWxdIgbJGKQzQ2gp0p6Kp6WaF3NAS1J65aJpmcm/PAbPWGAqcHoAfjYFijJlAWuRL34aMB850fMY8tsA9CZt4vb7d3UrwNRMm3mA4gIGa8KAlCAK4ALhg3dbhrU53EkXH/X5CI15FxbLfbAGfz+GIWQJMaIUxImoSpE1I9lxm833rKVzR9vIzb9a49DWB5Nc0BmGgZhEFZgCHngRNu6VaeVZkrumhO/d+1t+etoyJookSnwAEiH8U2rQByN1DgU+dgZMLiBnTjVVV58r08dn5A/dSGgoBU45mAkB3QnGXzaXan9IzshBVdLlwjN1JuqkyaAqFC6ZSfH5C3n24X+zt2lshUjYJvG619j5168R3/c6W7ftPOyawGE/kuGvfCAdhYlOKTECBRqaC5klG5daSiImBXHKgFhMrg+GKQO10Yy72/JIh0U4CWlDgjv64tJM/HsU3jag1gc/mAFfmyE9ZpcGZDyp1AmnOaEBubPojQpcAVwGLAQqkRrXIFM5OHyxPxY5nBrBoBs3MD3TjkbmxpWZD+awv0I+7/ZmeUmJKo98XZbmGLPtzHTwFTnwFDnBRm7LGT8x8SFVgfxAdrvBZy+RAENJkS405I61HjLFTIBmAQcM6Sp6/CW5rCoK9GpSBxkkJ2Pvcp0hRVM49+MfoqqwmJ+vfInlu+qxOrshFULRIrinlskL3RrkJqE/Rry+k663N4JQYdL4QyNecO4iqr77iRH6S/H4IIlN6yE+tAWkJ8fLTWedjC/HjWfiOH70l99z9qJpRLp7eenlZ8ZQ7wR2uJENf/0BD97zCvfd9gm2bq877FlURr58tw6lqhyCdhMWBuFgWJAXCKK55DDFw0BC1m7x6TLxz+mGcFraQGFdZoP0xKVEao9I710qBqE+eLoF7muR6TqfnARfnQJVKjhMCT2ca8vqQmvTMM8Pi53wOhBT4VIHnAdcBJyPVMWqkU4ML6P34Bjj3Y360u9zkpPjI5C5VzkyYEwZQ3sBexjy3CWAENxWpfCn2Qo/mavwgxlOvjhd5wKPnHKHaLDwTYZfFpwzi5Oungs6CNM+YYh2W0D0SAV/RtHxob8tJLcbQJ6KNikAfrCdOmJ1byZ1Io7j7AUYs2Ow7yAc7JNWtOYGOyxtJ2NsYeqqDtDd0canbrgZ3/U3YGyrO5Te0P/WyiH0dsSEf64FB5h1HZiFXhgfgO4w5AZgIEr/vhaiZ8yHCk0GZ3M13n3tFViZEaduHQyLaH0D9//s20Tr9rHgthuxtDDrn3oRYdhs3bgByzbRtSHuF0LQvvYZnnh4Lal+i3WdIXz2SDC8gozhDH+fFX5QYlLLDAu5DX3KgL54lHTGVZ6IyfXA5QJSEu2AnqnP4pIoBsuUjDGQzthUMQmqf7pJOh/OzYdbZoLfI/OVRDQDGEjDjBz4ZAJ+asKD/XBnLvRbcI8FhRpMUWSnNeTEDSF33RHIwlAtslsYSMk5aONrqsL508fz0o7GQ8/c35+gvz/BfGAKGakSRHKmQC6ogzZMhrFcCpw7SWFjr8azdRZvtKdptaWTZgRv2KBqQ3bNyic3STP7fyHZN51lflzWAdlDruCMjHd/tBrV48Oq78Po7MeuT8nvvIA3F8blwLYOWZBRD0j3UrQbAg6IGWMr35kwkzI+B23KTMyD3bC7To5iST4MRGWuwLBrEcCUHDhzFrRGYfMBSOngc0NnO9QKaLYpu2YO0e1dRNaOxudnyK0RnF2DFUkQa2rHTpvUTqpl4/pN5PiHFDU73c/rd13Cs/euJpaQiIFtyHSodGZ4cpGTbdAq1IG5mSzYfJesOlbig91pmDY7B78vij9us2Mf1E6FAk0iGzSnBKQapoQEFftkWQmvAb0xaLBgUz+0pWCCGz5dAycPguHIaK+GTBTUk+AwYKAfHojBQybMc8LFCtybkp7AT+oQsCSS3JdxdNhIYdGGZCzIJD8DO4YNX5Vbw+tzs7d3pC18LfBXBfLcwARkLomamSduhsIVqiwE9fNt8HoIQlkIFkdAp2x2IQdXd6BqKvYJhPwMpxMakM2fXUL/wR6UQhW70SD1ZAsiPtKlo9f6sd1O7JANG9qgzAEnl8HzzZCISKXzaPqBDThARMKYK9bIUH5NMTT2QPfAkPfNpcqXkRj0HhpwUgE0NkFvRPqTvYUSH1OfAlOhLL+Sfa2ZWJeaaUtXpU5iWFBbQaxyPMbKVTJ4O14h6tfoJ0XOMIsn0rKbNS9vwaVASpUFTPxI5hlAGtRBoGn42HklUDWZkojvgC6BGkEFBhKCUBr0fqnKKUJ65lQDcnX5N9GdcUZ4Zdq5JmB3H7yVKSZ743i4vga8ltSuNSfYBngFYMkcJYctV3CfC85KwnoTtqRhnioDpQ8B/7DgChXyLajOBHLTyIXexZDwSAA7M8fg6+9MWgREikIVBjc9V4BKp0q7Bl6vjas4c7PBmFAaKf5sSBXAtzfDy2PFkI40XVI2ucFcFEcnrjwXic4j79r+v01Z20gF8yoQCQu7wZAe5ZiFlj/yDubBKPa+ATnhDQtm5cpyOQlxqFQx44olsOxIJJAzMiVkysRJNZDnkaiEQUZK2UNMBDCQguVvQbQDphZBZRDdSOGsLZQ6kynY9IsXiLZkgsiDy4fNkH5Q34nYUifxeQIoLSU8eyL7Gg4M65uge8+7pCIJVFt64cxht1ORC+7gBBykwoxtqHmgO6O+9aal7dPZG6O1X2Fnm5QA/oBU+VQBahqUOGhhiIXhQBesbod72+C1OEx1ww+mwK3TZdzI8oAISAfXQFIKfpcNHiOTSW/LTpZ4YZkimeNpW2q55yFVzqctuba4kY6RYuTCMBVpM1UjHRejV+A00J8yCQ17LXleFx+55ydMePUv6LefOTKfxBj2P9DdCpuyd+wCYKVttr+wF5EWWTORpquo+vG4TbKjrCVS3QObDjtnhRhS0bzgK3Dj0l30NWaUgHVdSAxa5hoBmvBixTuP3NCgnqsC80th+VZpXR+N2mzYHIX5Xni2E+JCxiLaw0P3Gh6GGJzltj20fX0yhdnUKmdRiYBoB/FHXubxCfM4d/Z82X07zf53VuBAkMhkrpYq0JW5rzvT1CiMB0YK0po0L71+WS0oZUjGsaI2hikNZ0UB25Q7byppsOJgRaAjCU1CpmKkkUVTLsuHKyZArlsWz/HmSESELqQaOLgbpmVCMizhSIpPar1YMEeF8yx4BngDOMcjy5w3A/8S8HlVrlf5yHxNS0hJlIe0m3o4XPWqcCp0mFJqpoRgxozpzL32DjS3F8tdhv38m6h5ZJKskCIuI2nX7JaYweMhf4GfQIGP9n1HmU+jyJ2vUzOxgm3vNh5fY8eg4/LaHUbDjbsExNuSDLRG0Ao9KGUuCAm5Uc4wsnbWSaV/LPI5ocAj/6/Kh0jusZkIIKjBsukSWKYKKHTI/WkHaXC/heGkaSg15VCeLx0gmgfQpY52ShFUFUNCkBoY0jXsVILw1npK3TAhN5MFK6RhnkJ+tpF206FmFPlY4aQMs4lMURNVzUgzjwPLFlhAjwGJlASf9oegvQfq49AhpLHv1GFJEXxoMswYDwNOaFMh7JDarENIxHguUOKURVKM5BAExrSkdu10QG0OnKpJb9omE95OyniSF2kPPWnLvY1SyCF1I+d9FNnvsaZhe1owZ3I1Hz15BgCtzW207tmHEBDeuJIEmRt6M4cHcEtGer37+H0FZePLueCy81B08OdlJxOSvSZK8v1N+7Hofd9R1RWKSgJS3bPATtuomiLTPDPFO7Im04JKv9Sbu6JAFQQzSXS66yi/s+HpOkoWn4N7ShUEdWmIBHQ5Y085E4oy6dVOyahqaQmBi86TsS/cSN9pBrcXd8C8uSjBAKfNX4IQAmHb2Ik+XGaKmnwYnyNXfpeWCZACvUinw/CF1amC2yPDamkhUx0cPrm6J4GkQ5N87oaDQsJjhEOmR3TrshD+QaA6X2FpJRQGZX2XhC6RDmZKas/xiER9a4qc8JoNrgTYSakhpxwSfkRGJc3xwmQfLEMO98aMdJ2B9EivBTYgGUdDTpQ4MoRYx1B59xGvAejs7mdVvYRkNXZ08pVP3UZ432bib6zEZXJICh2KD+jQr8CboyGQRyGHBm6/k6adTdz/q4dwuRTyy4YgaJ68QnTn2PPFsmy2btmffWNZ0vtnJBVyix0jnAhGZxwReQ++yJQFO7ol8yXTsPsdiGRSLwqKx8brFQak3tSdond/M6nmPqhPSE/ApbVy1CvLoKAAcvIyZYodTD3/AlzbdkrpmDdBigq80te6vQfVDUXnnsKEwny2vPUCV1x0PjdecRNvd0d5vQ92hgCnnBOakPZGCqn2DKdcl0RHtaRheww2GNCg6JTMrQQF7GG7rMYV6HZAUlcwPDKNynDD0qkOZpUJFE1GAzRFOi1MIV9gfy8M9ErXuluR6RWhDgj3S+lmWrKSq20PabMK4HPAeEVuIG8BW4CZKkxGLi2vAVuREspiCF60jzEhbQA094bY2z0AmWt3bdjIxy68jMYXN6P1ZX5oM8RMOuzvgabjUOsm5vupdGsYmezWZExwcFccxSsfzF1UinoERvrfovdeRShDZlpQv/195qs49QxCXEhURDQpI5TpuMx8A+gclgSgkDEoBAzEJWRaCMxn98jPAmk4PLtPRgErK2H3flmyJ5XANbmSc754G0/+9VewbSeM98rsOtOS4qU1jatPIb59FztffJTHX1rO2rqDpDMuWRWpOU5UocCSqlyckR59ryZNAc2pElMEcSHAAw5TQ/e4yM3Nx1HQg9EdkypOEpyKBjEHsQS0hVNUlAlqCsBtmbIWoiJzaRSRgfNoMkNkayPML5f1DgK6REM4LbmxhhDg9cr1QlfB75BocBVpP+UrME/IId0kYKKApciFYR3wnCodKD5bAhIMhtLXj0UC6QFc3djKVmBpGpSSzBcGh9zfG5uHNpPPhvZ1R8d0/opMwKl/3w5UbVS2zCApWXb+OOl9M9L7IgVwKWgfOgNxcAB77WYoLoBoO8xYBj2N0L5XXugqg3SfnAVuFSqKoK5tWIoGMrgySCaZLVrS8Pu75XUZD12qrpGX7/0D45ctoOWh5yHeBmpGT1KAXCeJAy2M83uYfca5/ObeR3jsGx/i2Sff4O2GCM1JuYXSJkternI4XitHlQgD26ET0m30gIU/z4MShf62GG2inU9/6fP88Yd3Y6XTqF6pIx5sSBLMcTN7gZdYMkZaA8UWqE5IJKAsRzKpV5fBWMOUTD0+X6pkigF2HJyZYYoaEE3JsJpmyXoPiiJ/pyqQr0KVLWNevcA7wEIVltiSYTbZ8BhQqIBfDOVQZkMCKb18SPf6JQaMi8iFAFO+GpzQnDy+uT3cb6QAHzrvXBacsRCIQXoAbINIVwednX1s3dfDwQMd9MZlKogedGD2Z/cEx8Nz/28ZqUhD++xCPFPmEX9iFbxrQ2OrVMf6O+TymyHH4gtwL51F5L6f4ch3cd4Pv8crN9+JGR9iHr3YjdAEVtso6L9hjhwRw6Ju5Qrq17wpJdfOFqguhvIc2H0AFpbB5iYWnHspJ519Hv994DfMWFpKjbuZWQ9upFeFtR2wLiQZajQTKch8IlMBp27hzNE5/aSJ9Lal2ba/CYC+zl62rdoiA4koYCsk+0xKp/mZNj2HRKwfJawQTgqKfRDrkW5zvxtyHRKx4NGlnbZkujyXGoBQPxCWQ6i5IZ2WwV1LBcWR2c3PIT+7TfBbUuKEgCXAy8CDNtwKnJs5vxN4WMikvGKGAs3ZUAfSCdMC/An4Ti/4+zMnBZCCk8yh0N7xkgDySor5whe+icftZvBFC2Fj2wIz2Uvz/nr2bX6L11ZtYN26jewxu+iPpMdkkuHM83/LSA7ee+3oiIX19y3EcvYg9g8MjaRbBasNDu2iJjDbNxF/Zy/09aDWTiRYWAyHSqZa4Ahg9cY5+eJz6I1H2Pfa6qF2xhgNsaUjkwQBODRy7voYsVVrsHY0ghFFaYlx9qUXEsgpZP4Vn4L4u0yu9dKfq7CuQzDXL7NSG2zYmRo5CQSSwXwa5HgsKssLScZUdqw7eEjVsG2bFc+/PDSMHkHxyXnEXTEOGgP4dRunX0W1LWKmdDzm50ot1oiDGYdcL7idEHRJKdTfIbXhPLf0qQwkJKDVpUuBa6cySYJ2xgOtSuC8bcj7OpCg1deAV4APIbF3DyAdD2cq8tWYwxPpjkGCIYzz08BVMVjYAmoFh4qdzNKliz38HlWufz78CL5gPj/76U/wen2yhoaioamg+cuonVNG7ZxTufAmg0RsgNa6Lax48yWeeOQl1u1sIJQwDk2Rqfk+9vXHsYQ4LsZ+/37A94NBz0MW1m8eGMmMDgd87no4e86hU8IOY3V0gxCkNjXy2HU3Y8YTSAMDsKT5W1RYlF1N6OHbHmgKEyZNxK/7JUR7awQVlcXlMw5dbnYP0LO1iSKPoCwAHgtcFgRM2YOxBtI0IRTVyC0qYcvaRqwxICwel87JcyaCBRU+D44UqASIpmxQhh7F5ZXOScuUDgRhyVqZTsCIyuJMwpSVyixdlgJLpaRk8jhlfEk15U47bmTNPVWFkgKYlCtxdjVI6TQRWA/sViTG9DQkQzwsJCyp9z0unC3Af01o74d0l5Sq2FDdK+2y90q2bfOnP/2JO6+6iv1P3k9o61uI/gaEbQwzkhQU1Yk3UMykeedy2xfu5tnlq3j3zSf54w9v49ozZzOrtoSOWDKzvczx0fuXSDbv3YALeiT+37SHBWIVWZ933UZQMlyqAAunwMLT4Od3Q1cPRl9CpoMmMyVnbANhw7P3/ev4+2GYtL22BSstofj0plGDbjRfZniETdNrz9FW14zbDYotsWiKObS6jxXtcjsVXN5cWpsTRMfaexcpCa75xK04n3mGvs69lJSXENZ9BAIWflJYqQRW1CSWkAVUMEGkIOCTXjenCvF+aRqotqx8lpsLkVTGwZB5N0JIu8rnl+B3BYmGEIDLlE7LkCExpdORUuQJAZ8HTpXN8grwOO9dciSAZ4Hxcbg2DIUt0vmgdEKNeO/qHUhmuv+VVzmw4nV+P0GF0iCuRfNQLr8Rx7wLUD2FQxB1RUFB4ArkM2XRxUxeeBG3fy2NEemgsW4Xb736HI8+/grbG1rpjWQnek+MjfRevSADTolrGw73sYU8nlsta1O53BJwtnYDar6GPTDoIbQzNlSG2VRXBgdzFFLVscslG9D9yIsj3OulpWWUlmTSNhTIydXpTSuoQhDUoTUlfRkJJBONNQShtCDREaK1IzTGt5LShsnaVVu5/76/84Of3UFrNImSMMl1+fCYFiHDhpT0tgUEJPshoMksfiMpSxcbvTIdS1dB98gNC+PdEmunKhJVbakStaVqMiQXjmUqtKqQ64SIkckDMqTTYSGwEvgX8Dkd5loSXbGd96fGtADPA1PjcIYD1D7AlOffL+RUACvSNo9EbL6V34P+zmuEl69AmTwBz8034zztZhR3aSZUYyLdKT4UJYWmD6DlFTFt0XlMXXg+H/t8L2071nD/Xx7Kqu0TH+I9FhVWQcE4+f+yj8D5V419nVOFZWdAaZEsvTNBRcv3yKCMsxjMBMT6JDamairULpYp5KNJR/pyVRX8vsO/H6SWZmgcCtTNmjOLokC+/KCoFJx9E2mjgNZWqO+A5oiMrxzk8LIRwymdtDBG73Qwavky0ymqK6exeP7JzKwoZUYwl6AzB93pJR5XUVVZCAVDguBVG8JhWUixuwteC0OvKZMB9cx6ItJSWrqVTOqFJoPBcUMGhp0ecHvlmhW2JNpac0nP32SkSncmEjJ0vyVxwucph8ya90xJYDewKQndmcIudZb0Fp4IsoE/tsHqTolTDOaZ5HTWEf3Jt+j+zvnYBx4FsRtoBLsORBeITrBWQeopSL2EItbiEc9RM7uPH/7961m1+3/vtTN8EJwBvQfh5eclfmYsSlqwebP0+YYMlBvmYxwYANUNpTWZ2g0CSorw3HE7iZ/9TILThtNgTkMcKYnCkSP3K28iBPOgcSO6U+ejN3wE7VBlVgVc5fREvKzbBx2mzC7tRK7ex02jZuL+/TuIRiKcs/hS5lSNY197O8vXvkVfKIHmUKTbOgU+DzjiclN2RxpCqpzgaUuWF3aqQFquMZolnQyWIZtzOGQQdzDRcZC1VQ3aeyWTRdLyHpotvXNu4AwV3rShXMA0IZls23t55gwJ5Lg9BegRWNAPf1Hf4zgegQaAH7TC0zMhpweUAOS7Bcb2HaS+dCvuW89BOfVM6VYVuZA/BYw+2L1GIoULfLBtFaIzhNWlon+1+Rgt/l8xUsaxpk87G9O1VEKUD7wE3f0Qjh/uc3Qil82qANhB2JWAA2F4aj1YCkqFA2FXQ0sT9LWTWPFPuG4hPLYKOoch3QQyL7skkKmrdxRKDkBaJvBNnTeT8884b0Q5rnhzHdu3dLLblOiFPsYuI1CiQZcFukfDSMjp6vK68OUE6OsYhYPRdTBNXE4XmqYyceoidCtG2aT57Nq/i2i4j7J8H2pkgHxb5iFFesGvQY4PSEpb7UNeCVF0CJl7JFIyJOb1y/EUSM0XF/RHwHYAbqjrlUyYtmSaRa5bwoxUpAMlhUznqgeWZ2JN1tDrfM9kIif7KgH/bJfu9RNNq1LwdAfcOFHGrRQDnIUgehOIXz8P2zagnDcD0ME8SYrmRBf0NkLEAWYPbO1Ga8quvf8bRsoov1YyDKkdoMwH9yJZa7vIgoa3Rm4cpQNpUPJyEAkBoTTiX1L4K4UlVH/1K4TXvkHfz38rXeS76mWZrsQYJr+hQm4+KOGj23KJPmjtQ/PqfO47XyTfHxz6TgjaN65nQyJNDIlkcCIT3EZTlyVT5nMmBujdNZBpU0XPz4HOnkN90Lxuqs4+haYX3mBizXR8PtneuKnzSRppqsrK6W5uQLg0nCb4EnKD5qAufTAeTWLtkha4grIgv9cpJZBHzSQzJzITXpXSKWVDvyHBH+kw9CZA06Rt5fTIQv5Cz2y1KaRnsk3AyUhHwyqko3WsAPTxkIXE67UxsvzHiSQDuL8HLiyAolwk98ekv0HxCVjRDts7EVMdWANvguZEdySk7purQJ6CMpB9e/83jJSZPKJpAwS3g6sJLroaahfA9uXQ+A5gSV3C4lDETzy7IVM7SmNwL0lRXE7nH36H/7QJ0pK2BRyMwd+Xj80ohgVbm7LqZt6SCUybP5WrzrpkxHnbTLB19WretQQDyHUhF+loGOtRPWU+dH8AxAAAqXiCrl0NI66z4kmaXliOqqhceskFh6Sf5izESYjZ0+bR17CNpBJCU0KoCZOULYHuikumZmhIN7ZtS8fnwID0y9iAzysZJmVldnlIQ9iQ+VCmCuGUhCMaNhS55D1TQhZUiVoSDWEoEroTBmYqsF5IMP+JyOax+N9jokFal4JtMVicAn8QCEqbDAM589ttxMGUTO0gKcWwiVxJ/EIidrN82P9bG8mBDGR0bAN1HmxogdadQAZrM5gxOZwEI2FAddtJ7LFJ1DtkrkBrUlrIEYZiUWO5450alJRCS9sYACzAoTLzpmV8/PRLyVU1GaXUnAjb5uE//IIvP/TiiByco6EL41aS+JaWY4+HEEyYUMX55186dE5R0TQf02sX0TV+JV0kMNQQuh3CSIPWKyd9LCUzaeNAKCpzndwZ7J3HJyu0phRZ70F3SyM/kpTZs/GElCouVcadNA+ETVkjIq3CrpiEOEUzUYkUMjdzCnLbovcaf/+/pgTweA/MKJEqsEhBKCSLbTo9MhXfr0lbTfUgGcdCzsNBJsryYf9vvXZpoC0tl9At70CBGwK2LLjmYQhENbokja9EWsVkYM+2jT6hHO/sagDc/z/q3jvMrqs6///sc87tfXrXzGjUu1Xc5I6NbTDNBkKvoQVC8g0QSCEkIQmpJKQBCSH0DjbYxl22Zcu2em+j0fR+79xeT9m/P/YdzUhWGRmT5LeeZ54Z6ZZz9j577b3Ku97VXYuYjwwPBsB9ThDDkpC7QBWlG7wronSvXM34iWGEJ4TQ1efTU5P8+1e/w2S+fMkof2NDUHWOGbfmGgpcRDRN4z3veSexmrqz/99wUdPYw6ve9P+4+dVvo7NrBdfcdg9bXnE1NTUuQiG1F3gMtcjztmoKkrLUqZMoKIsWrzp1LE2dLl6/Ip4ExRwkDVUda1vqRNO8kHdUInZEKMPARCmrjrpWAxcnlvy/JtuKkPaoOXKqVNBPHVdlJ5VK9eQtK5PYrqi5kLY6uaTNiwiALyT/8+FvULXY2WGYfhruWgpr/HBthDPNzDxelQwBFU7qukb1NAnWwKKN4PFhDeQpPKHC1c64juarhraFGwJ1UNcK3iAiXM0FOY5q0ry4QzWFnhWfgNVe2u++jdM7xnjh8YPkyhXu/clP6D/Vx1f/8UvsPHnp+hWXobFlU8sCWsDM/XnbHdfz8d/9JJp29mOQThFNd+GvW8fSq9/Jq37nB2z4wPe55rMP86p/+yaLXnUDcUdQts+U9ChWH6ECUY5LoQ+kpoIMwgu6X71eRBUKlnRVnxSJqfa+FUPFZWYq0FBN3toGCE35RUGhsggChX54Ocy7/wnpq8C2KZgag2JSWdtbG1V1tyyo3neJLJRzqtDAESBLKoWQOgnOAmkgfn2m3ewJM8ukKZljCYoZtL5lJflN15J8ZIfCqdQE4Jkj0NSO6543YX7pS4qrobMD9j6oTK1KCfr3KarSK26He78DgNVeg7N/ANCh9W4ozEB+F1qoC5z82SdJ39DZ9xkW0G8y2p+g7xcPEGpfwnf2H+CPf+d36KitozFacxHG1TnxGALhCSIluGMupAVm9pztTKCQpgUV69u0dh0B/4tzX1alhGVl8QY60N116LOHqxEitv7NXLf8ZvSWz/Gzv/lvfNNFPALCerWPrKa4GnKm6pXkcyvMHS5VZl5xKc48d0Ad3BWgYCh/ZTyrUAsRXZlCWrVmyNLVCTiLqkrwa6lE+LWIDbyQhVtDVQIqWymPpUNqBurCCo8oDRWw8UQ5Q+3lrVXokIWcwJdHx1W1rs7EfAUQ0Ahc00R+7xTErTne2waUTWC41N+tAXSPC3msSM1rVvF7f/b3fPcH3+bQv3wPlrbC1s14vrkbYs1U+geRg/0qUGBo5+fBCzVD2Aejp6G+DVIFtFd+AtbciXPgBbRdP4RiH05uYCHDm+uU7Ahqrr+BcNDLzI7nyacz2LM1UReRtuYQ3/3JV/idv/gC97z3LZw40Ms3Pv/185NK6xAIBti+7TE2bLjqnBcl0rEZOvEEbUtuQL9AZbC0Kww88V888JE/onA6QYtHATMKlWqphFSnkb9O7bK5jIr22RYYbnVqOR61zyUsmCopsn5DKn4Haam/bVe1IaIH4iVlQiaAPn4tNHK/FllvwL+3Kg/C61J7snTD1FQ1silVmUkkqtIKHjegqS6tjgTfU5dWkctTpHnFN0a7l8hbe5iZHKT+g3cw9dXt8N3xs9P8S2IEvvh75O0UHN4B//485B3c71yPp8FH/sv7cUaKVeZUr/ICTTdkckC52mjVmcOOVE82feWNuJZsobznQeTYYeUtWx1o664Ew8FJ5mFsDxTPOX0WJAJ0nfW338o7XvM6/vz3P0MqdbHQgpKGujD/8JUvEO5uwygFeOsb30jqQg26NNhwxUqefPwZwuHY2a9JB7s4wnjvPgINq4g191zwmlLajP/40/zoQ3+PnpbEQmpndUylTGZAmW0BA8ozKmQez0IgolBZZlBxkRekivqWLBUW92uKA89ylK/lclebi0hISBXXOcb5w///F6VRwL+E4Yq2atK6rFzYXBZGkyolkHcgZkBrFILhavJbA5cXmncuwN+9rDuSqLMyAFbAJm0mkTUG8eEkoiGAaJ5nKboBvUyhPwm9IXg4CUaQ9g9/iGvf8GkqriVoN69UJ8HKLgiGCf/mWwi96rVQsx4WrYUGrwKKzUpDFFwutKCLhpvXELx2i8pEWkWQJ3H2fxNn97eh72cvUYmqg7Qt9j/4EE/tfp5P/PEfXvITQkABi7/9l69TKvn50Ac/TCp+EeVzYFHbYkKznTbO/jY0w00sFCEzdOKi9+kUxjj9/DPgkeQNmKrASFFx5k1akCyqGI2uq14GLo+CL5YEFDzqZMmKah7ar/whj1udPi5Nocs9GgTdVUNEKoPDQDWT+P9L0CEpYdBSPqA0QHrUWBy32rsHHYUhPGnB0TSczENvDoaKcHz6kl8PvBQfSaCKslb6sAbG4YDE+fZjuG9qRru9ndK9g2qGr6yDcgk5ehQ23Q51AeitkDx9hGTnrXgGBdlnh9T3LQnAoQL57/wc/c3vVEUykwNQKqh4re5GVT9aYJqYOx9laO+TyuwLVFP3ZzKdKICZFoBiSdUyvBSRkm1PPsGNv3EPscZ6kpPnn1HDrVOzNMLym2/mja9/N//0+b9j6HDvJRGYDc0xhDjPPiYEslyiONyPbQeVc3KeAIaUFfLHf8KhHQfIFGGmrCzpCaE6hBoOtFWgq6JC4MGQ4oRwvKqzX8pQ9FcZU5lwhqF2VbdHkanoGoR8itI4X6kqn1DhcseGOqke3SzD7P9lkSizdTwBWa8qOqg4kNUU6FfqyuwdRPlEyTyYGRXRDHrgpgVc4/KjdhJV9rgtoyg1azVYrFO5d4zSd/oV9sOFMkZPVGDfLrDjeD7xGVi/itxPnuXEez9L84QXoxxU8dUnjkHfBHawQqU2BjW1UKpyldkm+DpAW6JGOMv4Y1vqnG4+O3SMDZQ19CuuRvyKBBjZ08Nsv/fH3PNbb6JneSN1tR7a2mpprPHicws0AzqvXIrW3EzFF+XRffvZ8dAjZ/tF5+qAUD+r1644Z1Jnf0AYLlKpacZP7sUxz0czIpHJfRy491s8caLIoZRa0OPA81LBeR4DfubA02WYyavIf6YEwq9IkgoaOAFFl+HXwF1Re1LQUFi7aFCZdr5qsZXjqMVSstRuHjRUycVSFLPW/2XxosY2mlRojoGkYmkqSRXN9IVU51ZTQFKDiaJy9ydM2LdAEODlK5JR/VQDsMivGDlchlrUrV6lWNMOPDKqYqk3boC2jZSPJ2AsB6ZDcfsRTvz11zAPJFQKvn2FCjlls/DMw3D4SdWSwV8NuFp5NWJ0oIQv6Gf1qsWITBFGE+p+or65e7Rs7O2PI+eVob8ksR3u/df/ps7v5oVnn+A///xOtm/7AR9/582sbTfw+wyky4UZL7L3qz/gyX/+CvLcLr/nmte6+qlvaJx7gzSRlQROQSWLpctLxSPIVvKY5vn2e4dK/DSPbRvgcFKx/+wEnkJxcs9a4DPAcxLGHUjbiuEsm68S+GhKgSI6iGK1KYSYo1f2+1C1V1q1jy1Vov9qzMmLCov7UZ34/nfyKAuTdgChopPTeYhXVHTSNKDkqOhlwAVRTc1T0oako7gkrHOf3wXksscvDA3fmhih25ZhDLpgyoK1Ydjsx3d3C5oH3Gt8rP79a/C+v5Xg6iY48BT68F70ShULZ6EicakCLG5UzcIAAhLMYyDyiOZ6xIYVgAOVcTBPMRsnKpYLHO8bUms0VVRmVOqcnft8dUcvQaQj0Yt5YrFFNLgt6mNN5MczTI6aNIVdDO05Sr5QopLKkum/NEoYS/3s3LV37v/MFNbQs9jJXsDBTA+geWxii2KYxXMJ3yTSLjC4+3mODOcYQEXQBjk/5MZAZfFFAHCpjhZBj+qWHrRUK86oC/SKQpQbpnJvM1nVRKRiq/CwIxRSwtHVdJesavce5sgj/6+JQFEs34A6eStV1I/pAdOvgiXxAsQTqpVo2lY4RQ0o2NXUwAKvddk+Uttreuh835XoHbX4R8o89LavIu5N4g56kdumeMU/vI7+g6foH5ig/X33sGj1newYHKfypa9jZ6sFbg7qXF3rgvAYTAwqFo/XLAW7GR57DDk6rrpaeNzq1AuGVTcKQHdpCBdnGvhiBFTw374Q29o5IlCnaGVh/lMkUg9oBCpeNPzMpLMMlkCOqmydmR6bIzy8hP4Kr45R56b/+CyESCBdEbRIDC3ShbSmyYwPky9UEBpkUjME6xaf9R2y0M/pA/s4MV65JAdnABUQ1d3g8yu0VUQHr61KK9LlqnJU6fX8HkWbbHoVl7itq8WVS6nPuF2AR5VpFB21u09z8Zqsl1tmg8ezxpGY9zP7RCMo+rC1QIeh5sHSFd96Nq/YY3UNpE/Vdg1YiuNcVE3YmKEAKgvdji9bkcaeOc20NcNt//xJhvMHkQ0aWl5Dc2vkD6XZ/uiTVB7JYNcYeH6rldhgkfJf/iP2CydhJjfnM7iBlQFlZ+yOK1PRV4Q9/WqUhRJUsrCsEw6ehNTYmXvwug1am/2cTMehNQbTlYWdQAJl3OdsZdssUBwjCgik3wPCwSsFPtRudQbWJzl/ut+lEbiqmfyuUShBdHUbq193PeyZDR5LcEpMDRxH6kepbe0hMzZEXTiKz+/D5TFeFHAQuk2kOUbQL2hMyzNQHjibuFGgOt5Jj7rXIgooYharnqaj6phstzphNEMxxxZdyrJOlVViN2+rvJOUCm1uW1XyFB2mBcT/h7OzUWAdKublQp0cMZQyW6hTpwZlqi4PKSXSbNVHakaqcQupggyareamHlWWL4X6zpIN/vNhNi8gl61I3jY//qtaeOrRX+KvhPEub8FJOOS3KbOmdF8GLAv9TU2c3PMAJx/5N+wDQ3DaUsZ0EWgVUBtU5GvHTTWSZcCO06pIRUdtBaf7OW+7adOi1ePmFOCMpc4PQp0vmlDvkagzW0rF5LoQMXT8DW2AxmAqR5dj0T9TOWPWnJG5WMEZEbrGojvXkivmyFe3bF9LhIAMU7SnsRwLQ9MRwiIadFGcGkObdlPvd+Gq8+MO1aquYi8ajwdvXQc3XVvPscNxCnmHoaRqNBaHM03D63RYUQfloApwWl6VU/KHlBVNUWXzDReUAuqAtisKCpQqqTKNpAWmriKBWanC4rpU5t6gpfJK/9OSQXGWt6AWfRl1Xx5DKYPuhnJJNVuL+cAuKnhUTqhQuGOq9zlSVR47Qpl9tlCnkSnV614W7iNdliLpXui+oZXC6g7GBhKkP/dLhZQ0VLLW1eOmZnMrLXevpq6pnki4mdTSLTz+F/+EdKwzjKIYOv7330xpeALn1AHOtNs+JudaImZQGUQLzq1+cRC4Z7F1C8gn6y1h7Gwe0hbEggqpeCFFirpUTLh6wGm6TigYBJnHVcliaF78fnHhjUoAXgNRkbzt9z/M8f6jDPziwBklG3vkMGOPH6UuFmNyeorWxhaQOt5wDSKVRJQcAu1rkb4QWm27An6dK1qMzrVXcVOxSN2iXYz1naYrUSKfsilZgpmUpDEEgTDUxEAWFcmKrw7IKuSCV6hFJA2FFPcKZQhYhoIYJZKQ9yvMXsFRp1PFUTVKbkMtzNPOwqFCgrn+SqAW/6w55nB5kCMH5a+VpPq+IOp09BgqaKADoXplwpVKVYR8RSWes6jfPlllXJJqjG5UGiBQ3R01qgq2wHu6LEWyS3DoS8dwjeYwbu6EooVrYw3ungD5bw+hmRpEbQ7f+zhN3i5KAZPaph7VGVgHPAIR1ZGHLQof/sUc0juFql9uRnXoylPtcGWed4YrlsVgprRg+9UeSZ/5W3MMnEoe9DqItOCVo5h+iba8EfOJY5Ay6ezq4Hc+9iFS0xOMziQgNQpOEW9FR8MkdyFmGU2w9cNvxttWy66v/JT2Rd3c/7XvnwmH641uHMtGJixKokg6k1aKpEWg5TY8DUXQAwjhrppy519ewlVPdPEmNodiLF5xBQNHDjLat590YpKwz2YmlaJULGEVLVUZKlRuyC3UYvHq4LZAVBQ0ppSDkQT4A4plyHYU9tdC7ZPxUpXTAWXmSXOu4mAhIlAJ3ADKBC0z1+AZLo8Gmer7npMqv1Og2pG9iokz3GDYynxDqmtlhCq4TEmVucmjxu+XVXJdeYbUjYKj7i2AUrKFojcuPyGrg/n4MObpEeiU2Pk81kkbfFAeLDH52BDaa4KMnZ7EfijJtH1S+SMzIAI6TW9bx/iX9yHcIKecuV4oJ6sjDktVOunlTIQLACFoe+MNjD70LJotmMrkLvvWAVxtPZTzvQRW3U3h5FOElq1h5sQurENzSIjf/K3f5GPvfx+9P/wcB31pWhp8gBvpeBBC4pYOxrxbOzM1zfWc6h0k/t/3YRVL/M3HPoU9L6Ch2Rqy6CCBXC7Hn//5n/Ct//o2huFG4FNkdGfJ+ZwuAUJH8/cQ6ewg3JyiqXkZEz1LGD65l8T4cYqFJGbOImqAp6KIT4RQO7bbVV1EXtByau4NXfW4zZTA8qjHURFKabLW3L4m4QwV++WcIC2oxR5DKczUvJHlq98VQCnnxUSggggelILsQkXkDBTkR5cgXerkjOfV/WeAlKkIXgqoPTsO+BzwV9FnOlUgDgrDHJdQKy+P+/SyFclwGTTds4bxxgRGQaeGesb/a4+6izrB6tdcTd0r16DV1fPkB/4BJ16ErTUwWUTuKzJ97xEFRVkRoDSRPbtCbkzOee8Bj4rTznItSEniif3IvIVpS5KVeaeCgfISkeffJjVNVbdVCpQP7gJ03L4CBSvO9J7j6j3VpxhtinLrHbeB141jTrEolqKx0Q9CoIUMHCqUbBMfarcSUR+yaBG97gayR48x8ejzUBMGv4E9ffZ+ZiZKEPIRaI4QiQS5/2cPcPLTJ1m5cvU5NyyVxWoXELr/PMgGAcJAoCM8PnxdUdqDAUhPY40dxiVNYhJEGjx5iPigJqgSjrZUOzC2QiiU04rboVRWAVITZdLYmgpA5Ctz5s3swrqcxEIIWK8r5iNbziVv02oU2NXfs/++mIJKlCIIVGzKg1LKENWwtgC9WgLhciucYLqgihJ1oX7nUEsuRPVkqn63Uf1bVsc5hVLuhSabLzuPtPTKNkJJB3nfNAwXSD7XD36JtjlAwxeuobguDHVt4KlH1nnV1vFYHAwPrjc2YES9yLRD6fHs+Rf97EwmywrXPysCivEU2BLNpfBjZ8Si2v7uAjctBMSiIHT144Lks99Cls7O0YTrI/zL1/6NK5ZvBM2NJYJoFYlRqYA0EYYNwiFlO3PRseUtiMYY9UuWKcYjKaGxpVpyeZ6xFco0dbTxm+97N4u7OmhqbDznDXa1KjhNduSQYgu9oAgQAqH5MKKdeEwHbSRBZk8F7SiYRyB9CKyTEElCi63QCD6XUhY9AG6/KjcXhlKcXEkxtMYzimSyxJwiaVyeEglgnVA8Ey5dLdRZky6KMjqgug9y4cXoPuc1iWIiOoHiwxtDKUWyGmEUAchJdd9lR4X0Q75qpS9zZe4ZlAJnmHdaSfWduepraRYml30iHX1kAIDAa2tofFU7pz+yBza4cJYYaCtXsaL5Op449TDlo6eRJ9Ow2q/wFjtnMKcF5qCcm42LiYM6r92oqjJzzmB1pEoWXrBJDygigply1Zu1FR2ptMHQcS1bhHl4HoeCEAgh+Ohvf4zfuP1N6JqOY7vxuOqxK8OE/DFwHGRBQ6tUqFQqZ458zRfAdhfo/fd/nTdJx3D5Quc3CyyH/t0H+W4iwbe/9Y151bESKfMgy1hli6m+Q4wNDdEmIjR1LD/nUDrPCSUFxw7389j9EywqQY1UO3PArXyhXBJqvKp8wPABOhTyKohAEayK+pqKDVMFVSAYt+dML8H5OSouJi7Uo9N1VT+ZsdQC9aIAs1r1p8xcceL5xOT8y6WCQnVMMWfi5SsKjYGhNgR/VQvL1U121tB2qj8mcxXlGur+AtX3OSzcD3zJyI78nhkCabjh796Ju6YGjhWZTiXYlzhCZWoS+7njKlW8r6Q8Vh8wUj1bq7Oy8VWrCNcFzv7i+WvEh/JSmznb63MUifzcKM4ZhgHc0Frl+EWd61YZ2tsg4Mc8fBpPTROeSPU08Pno2bCaj7z/g+ja7FEn8Go6Xq8fzadiqPpsjHQet7i97zD0jVfvQ/2Ktjfzjz/4IXWLlp537jQEv/2Bj7B5y41nKmqlY1MeO0ni6HMcePoHHDu4h8mZOM899XDVMTlPfH2+eOq5+kN/xt2f/gCLr+8iJZRzXdQh74KkoUyaVBmmkqr7+UwJih7QwgqBZXiqWDoPjFZgqDJ3xZcC/TVQUJupKs7PEVWYUfX7ZhWkeg5fUC6259oon6cXZerZlnpERUeVkZS8kHEgYSv2p/OBxiRKKWdP31kFKrLwRPNLh0iNQd1ABNoFTW9aB2kbLZ1l7MkHsQ72KYx6GUja+Bvr1WiTnDVjXj3Mik3zFluDgX5t/VxctMdQijd49lQKQycYm6eA87frqE8RncQtFQu1mDPG/QGobwEEK177Vlbc8QYQAt2q8IG3vo2WxtazrmNaFQJBL7rfD9Im0ugDwyCI5AwcNuXMbdnVnNeKZUu5ZetmPLHo3JfVBECA2+3iM7/7Id7zoQ8j5m0A0nE4uu8g05MztC3eyOZb38T1d76Dm+58o3pdCqRjIct5rHQcOz1OafQApcnjyOIUOBaB5h42vf8j3PztH9Pzls1MOTBcUCbPWAGOx6sKEof+ARSlm6tKcqIp88usKDKVJAuHx5xPdKBdU6B/qalFmZZznHizwJQMaplcTJHOF3KZL0VUZ8FZfH66WEUxSJjS4ZSEU1VFutSGIFHjtqt/L/QUfuml5g5s+7vH6YovoVQy4ZSN+d1taGubEE8lcEbNqjcOhd1TSs3dnDm/dY/GgX17yI/NM4CmLex8Qnl4bh1RNlT+adaAdtT3CbeBHvIrBCKoeC3qNZZ1E+huxhXzkXryF9VRCggFYXQEWjohWMOB7/1nNUkruer6q/jABz50Ft+CEKAbAqdSqabBi9S01VIxJfGKdfYD0YC2kAJsWTYzjs33Tx5gfPA4xAKqw3KhzKrlS/iD3/8Ub3rrOzBcHtWb1jaR0kbTXax5xT0YLg9CM3DMPCce+zHJxDj3Pr6LXNqiRcsTyGWpTSepCdsUzAwyGOOON15L6Ja3YYcWk3vyK4Q2LcEbkmR0RUM86YfxtPKNYkIttLAH4ikQ3ipQ1VTkILpHTUv6V6yNmHXUc9VwcqH6+Gb9FKv6fyXUor3YAl9IhLAEbEfRKhsCiqaqtcq71DJMSKVIC/mu2Qjl5UzBr8bZYEP/13sBEEHVk+aqe95C7J11PPXj75H7s33VQEB1oc/TGbvskBs+51YlKpCvwTUfu5WW9cv5yR/9K7JvXj5Jgr8mSsfqFRw6/eS8YVhqBew7jljZiFmaF0wtOBCOQHkaJkbhutuQD/8MHBPN0PjUJz9JOBh+0fCErmFJqU4OTdK4ZC1lDEzm7aBu4PWLYPfUGe69gVKBf/vOf+OklT3q83q4+3Wv5i//4q9oW7QYKSWFdJJsOsnU+CgzmQTX3HAbLu88/gZHEg7WcPyxn5A4uIvnD00St1SniJXAFTUqpCuNBI9Mj7LsxCFiXQ0ceOApWh7xcXpHivoaaO6GYwkYD6iivxYLwo2qvNwuQL1Qs5ezFFNrxoaKW/HevVRxoTgWpaagNmVbKc+sIs3O36wfstAQ84UkgjLZJoAjAsK2wspVbIU0s6t78eUMyeLiAZBz5VcnP3HAU+/hrf/6LpZduZV7h/dQxOIDb/0E//qz36K8K6Xep4FrsQ/zVPGi20LjunrcK91E6gvs+LcfI4dNqPMqLF1CKV4hO8PY+NF5n5r3KCwbJ1+h9OTB6nVVZIuJWWS2BZ4C/tfchbP3BZbVRbnu6mvPey9SgOYyEJoPaUlOvvAsLVevRVTO2QAmZxTmvnob5Rf2MbX3IF6fm1tvv4GPffhj3HDdrRi6TjmdYHi4l6OHT+LYNtPTU2y4agvGuTkkl49jo9Ns7xtnSU8zJ05MkbEkw6jQ72gJ2sOQLcIze4uc6ttLUxt4fDAzUiSZUhWzU1OQ8kIxpJFIOgxOQo0b6kMQcincnaWrAIDbpXbwsfyv1hmiQVcciykbgg5nKAtl9UnNPq3LgLJdVOZH1iYE5KVyKw03xJMq7J3k8sYkuTy/8GVhEdItjXR/nmV3bWGNUcu2x39J+LpOnFo/uFJnkg8XVKJ5Jt90bwKf4+eJ7TvxuH0IKZBb6mCiAokpAHwBP0vaGpjZNfXir3Og8KNn5l2niouvj0JXuzqRtvZQ/Pq9+Jq9fPZzf0o0WHPecdm2g9fjRdPcSKdIKlWhDckyKTnBHB0wB7JzdsCSKP5YlDfd8Wrefcc9XLl+C0JKBk8dJT02hFcWSaRnsG2DYr5MQ2s96zdd+yIaL6uQ4Ut/+/c8su8YUTWsM8DTFDDkwDILxosqinkyB1qf4hjwSJjMQNFSH7ADkBhzSFiqi04CKNRCZ53qs5QsKo4HrQxl99lZh8sVN+pUE9Vyi1kExOwJbjPHuvarnkTnk5KjTLtRUyHd05rqHffruNZ8eVkUqXvDIl53590cTvXx8K5HGR3tZ/prRzGPTMIqAw5aFwdUzdvgnbxD/lCO2//07Vy/5Xr+9N2/R/nJkbO8vrraKG99zZXs+fnh8xIMnblObTveq26h9OgPFM2or6zQm3//X8h4jru/9Cle9Yq7LshF5yCq5eAepMzjD3go2ZK4tAlVkU3zkysur5tXf+QDfPwt7+GqusVYqQROdprdO56lkk4R8RnkAj7MWIzyRI6ibXHL1lfgmvWXzDIVy2Z8pJ/nt/+SF06oEP1suNiFWqh54GQJJspqQdarGAjlMkzlodYDI6bamVsEiCp/W9FWw6/xwtKGAB5PmUzJIl9RnxeO+szUS6zOF6hetiWpHH3LmdsjZ08fgVp0s878y3EizZe0cnupWCqEX5a/et+lhcjLokiHnzjOu255Ew1/sZXaVSvptOuonbTZWbLhMBceiU4VbnvO/zfV8dAvn+Hhb/4UmSyoJqMnHRXHBEr5IqZV9V3OrUjVUFiYogWWl9ILz4Dmh43rgSyMnIawQagmxHuuvhO3fuGSNNuyKVQKgIYjK7gMnbLlMGjZFGZT4CEgoUzA3//TT/OZj3wGn9sL0mHs8B6EZpOdHsCWEuGrZXgkw5GxCbKpBNdsuIKGpnaklORTcQ4//yyPb9/Bv3ztv8jn82QLZQQq+VipTpcfVRph2EqRvTqkNMiXlJ9jORAvzi3UkRI0uBX/QKNQ5tzVq8EXLDKddRSDkANejyqZKBsvffcWVLFqmqrbFChw6Szp7KzSuJmrKXq5F7mkyl5tvfxKejF5WRQJQBYsVrS2YZgaO57aweS2JKTB3eKhMlRWtcwFzjI8Qz0RIt11jD7ej6zMm9IbN8CyeuRTD6hVEncUxqQqIhjj8EBJmQuv2QovHFV+CqgnU6xeJH1K/a5bDPkMnD4OloNxxWLu2nA1W1dffVFmVB2BmVcMl9IBrzuCZgu8jlSLzUGRH2iC2153O5/66Cfxu1W+XiJo6FqMNDRqMzMcH5nm6HCS/rEUz76wl3I+y8c//LtomsCyTO79+bdpCAa49+c/Z2JqjihAMpd3dlD+QMlSUbEaH0Tr1UkzMq4QCueKJlQ2IKArqnRRgJYC2DiM52HGJfAakpQNKUNnpgTyJfaacFB5cxNAU8oupLrX2ZMox9mK+qso0vl8rJd4mP7K8rIpErbg5H17iD8xhFgZIfbKNgrtJZxJF0xW1LY0+3yqM5A9nSY7lH5xnPHnT6L/9mtx3v4eAoEwuV2Pwvf3VtPwIDQXAU9AYcZ0Az75m/DZf6r2dJzvelanuTyhaDZTWZAQGJnhg//5EVz6xYfvdruZSiRxHAMpBW6XB8uCbj+czlaxWc3NeMN+fu+jv0NwtrQDQDoc3fMcwyOT1HV2onvrmRodw5YBpofGsQsJXEIjl07z/O6n+dRnPk8w6GFgcOrFN6LrilfLVCnMsqFTtmxSRYjNqNNmPt+Khoqv2LJaGuCCpjCMj0NUwsiMKuCrGJApS0oSygXwxDzMjL30cJ2gysZaNeMN5ha7jVIaL3N5o1910c/mpP4nT54LycunSEXJ2FeOqb9784yGJ8CRiOtisExXfhKgt2hsePNajt9/glxf8fyZuJKNHJxGLm+i0rVSdVBLVOB7e3EtamfxHTdRW+NXedhfbIddR1WlWWE+ZsiDmuoCZHOqZnpNLRxJcvNVt3Jl47lA0ReLZZqYpgVCQ2ge3D4fGQkjZVXPUuuGgZOjLF69jM1rN8473SROMcfh3fv52cNPsuWVr2GsrDGVs5maSJGeHqFSTPOTe3/B2lVdfOqTn2J8fOYMpfMZ0Q1EbRPSkYoq1TTB7abt1pvJ5kpkB4eYmRhF5uYvfoEjQFTrtCwPTBqqR2ykUSAMOOkC3ecjY9qkM2U0E3yawDL9FAsvPQ0rqUJvpJr62dbAs8OqVqnjoJTopZh2s8hv+PWfPrOKuhB5+RTpXGlWKEh7+8xZV7HHHUYfGcNOOReeRdvB+d5TsHcvlfofE/m9D7LkHW9j/7MDLFvZw+9+5KP85Iufx7IdWNGmStitc4bsa4DQcph+TNUJlApg+DGCft751ndc1DdSInB7PAT8fnRNp1KcQdM1HNthqiiVLV59kus3bCQcPjsPJQw30cVrSTjPc2Aiye7eYbrXX8NEaZxypYS3ro0jMyU2RqJMzqTUh+bNh+H20rHxeoZnEpiJKYjU4a5txLFtfB09iGCQXEsnmlWBU71Y42PqKPC5wSkjCxkoFrHcOjMalIJhil4XCVmh7FSwJy0KMxXCYeXEROpryRW0hdRJXlQcOBNFmP2q2SdToZpWnP/eBYohVLXqbE3T/0QAwWHhCvLyK9LsNjNcgS0BaJLwwrxdTsL4kfOYL/PEe42PStzGOZVF39jKDauWUTcyRm+NRjo/zVd++B9Mn+5VD92w5rVr0VR/WdOLUdeOLMSx3X7V/ntClbo3v/l1XHXlVZfupiAgHItRqqlBNwywKmi6jqFX0DVl6+eqxn6eMjbyzGRKKZkeG2b3kSP0bNnEgPThXbceZ+lSZian6X77p+hcs5opmaauufW85fQef5DVt9yB1yqRNUt4TZtGl4/aSITFa5fxs//+KjWeIGVbw9y4lWitIoApTU9ippKITAKyM9i2hczmEb4wyck4TqYIdlnVVktlLfsDgrrGJgb29F1qVi4tFwHnzZ5YL0VmI32XC5z9VeRy7vflVyQParRB4GRBFcnPioYq0yxdfNurnKggIwIWaThLHJ748t/w1vf8Dg0rltL7zWcZfvIwjSuqqOkDE9VaqBaFR3nDJ2D7j6AuBsUgrFgOz/5c9e04laGjc5wab+Ci1wcQCNyNDTinqvunEIhQEEGFNkNF0vyGipQZeBHVHLiUkt5jB/j93/44p6Zn6Lr2ZnxNTbhmZjj4o6+RO3mcooDpHRGQRX5qJ1i6fCnTYxNnXb9sl9mz/X5yhSJ6TQSPz0dFczGcy/P0f/wtyeEhNK+X9ptuxzQbSe3tpZwvIPxuQl0rWHRFN4tafOhmjvTwBKMn+om7+shPDKFjkRsdAyTCDbFoLZatk71Q/6j54ncrxpCyDToIn4YsOQte4S/1wCtVcXpnLlO1FxuWtjAzHMcq/IqYpgvIr1eRLmbczo50Cl40bQaIcBXM7OL8UFzASdkKfHptD3LSi37dbdz3pZ8yfd9+hDCQto3h8yEE1VNJQPsS+I1PwM9+Boe3KdKKcLOCBvmbwcmCx43X5184B5vmQdMMEALHsbH9AQppm0JJ7RduF+iBICPpFCWzgt9wcXjPc7zl3e+mr2+Q1dduZNHaJezc/gKHvv+9M0xHFVC0qMBPv/Md3vr2t/DsE0+fuazXEGze1Mqzz2zDqYbiZnixOKUigw/fR2zpKpbfeTfhjsWMDo4zvOMwhx47yfDaRcRqQ7R1dbL2jitwKJJLTrDnl/dRyhXxBgwMn83SJSvY+9z+i5t1uobWswitrR6rkofsNCRnkEX7f+yoOGO8z8bObVjS1cPSO1/Lf//jl1lgP4hfi7w0RfKiYrLnU6aLKVkF5FR1sBdKVhigvakZ/X3vxKy5BvZNkP7PH5E+sA/3mqvx2BrZHY8yeTx9puyZsoRTRyA5pRqMoauLZUYQq9YiZwQkd6Df8XaMFecpuLuAZLM5yo6FEALHdrAtiaZ5SAu1D2SETtu77mHVNbcjhUYll+Fzv/u7HDl6CsOnU9vZjC/mp3/X8xekC8umUrS2NCE0gXQkLl3w/utaWL8sxvNPy0vviI5D8vghUr3HaFqxmtZ1V9FwzQYmxy3iM3Hih+IM7x1A1LvRA6DbWTo3XcvVN97Oivoog0f3sOMXD5FMXRjOIDw+9BUr0bracYXdyEICUevHigVgaESROMyG435da1ljLrs7G60Adm17njfd8VrWbVrP/l37fk0Xv7S8NEU6X2BnlqnvUiLAFRSYuQvMuA1yMof5118D8Qwcn1BNbNAwswXsYgGkxErPe/CRkMoofvGT0B2FFk2RYQOy76iqo15+HZErVhFd2bLgYU6nEohwHRgG2ekExUoRqefJWrOsppKhx57kZ4/t4m3LV3LDmlVcuX49P392F66ggTvkopDNkpmOX/AaxXKJQMxHMOgnn8nzsVtaeceqEJ+77yCWvfBVKW2L8cP7GT+8H4SG5ovgCTeyeMNWjLoWXJEgk+kZKiKE31PLos7FZIZ7efrnDzE2MPCi7xOGCy1Sj3dRO4u2XkvB7aZk5tBlmYJeITUwhnAcZNirElhRDeKmArW9XDK7njzVxPt5Cjkr5Qp/97d/zzvf9276e/tJp1Iv4w0sXF4+H8lBKZOfi7JYhOrchDrdjO3OEajzYpVtKjmT5RtayRZKjBxPIJ/OgjcLrjysbYEXJqFoI1OpeU2/qr91AR//MCy5Gv7sd2CdBeUo/KzqcwSjkOqFoeNkn3yBlO+KhY1HgCfYjLSmQei4vT6KskBudIA6R1IU0OR2GDoxRMLjZ88LL3DD2tUs6emg1a9R01PP8NgITuQ0ljnv+J0fv0XRfbW2tNLUWIPwONyyso7f+/FRtg1XXvrmLh2cQpJiIcnhXx5XiqVpSKGBy0sqEmF/uUQ5nUDOOymFJuhau5au1VsIL15Hpb6BRd0N6IZgKj7JyNhpCtMJpjIp8kaQynhc0ZXWBlQ1XaCguLwuVNK6gDlH01QGWdegYmL0NGD1xc+fba7K8MgYP/7+j7n2zlt48Hs/+V9JLL18ijTrv+c5m7DsHMlOV8hOK8fwttdfS0XYPP3D53jvu9/JV/7zO0BiDiK8tQliHoXCLKI6U4AChjkobt2oAYcPwL2Pw/QEhNfBqeE5E/P0fvWZ9aswF3VjxxcemQoEo6TiCrcsnAw+vw9p5zCFAlpkciiTrZDjiYd/ycff9x46lqxgScxPIpMhPjRCsHkRtq3sEOEWRFdESR5KnjH4ZxIzfPnfv0w+X0SUKvzWN44zkDy/4+wOGzjSwcpeZuxLOjizUCqrQql4PpZwkI6kUMjRsLiRjdeton3xMpr8XlLlMoOpOvobYvQN9ePoNlrIi9XZjZlNkh0boXx6GPBCwFZw64sFlGbzbUIoxQl6VSTRthRpXr6KrtXBTmcU3ucScvJkLwMTI5c3Ly+jvHyKND9wUIeyZ8cu8N6q/Ow/Hkd4BLIs+eRv/9WL37B/EFa0KwYgCqCBaIiy/hOfpbL/IEe+8w2V/YsPweSEalN95DhUCnMUnKBIrrcshvQwYun5y79fLAIpHfQq+sGxdRxhYElJWVO6bTkoQsmsxVOPPsLjz2/nputvYs2aZfRlpmgOBpFT8TPUXdKSlEZKZ20wZrnCN7/63XnXvXAKsJL59QNgJk728b0//Tw/8P4dsdo6gj4vwbZmpM9HfUcLoiFEe1sN66MtxMcnyadqyDe2MVDTQmJoGMeqqPlPp2G6dDbxgaGpZ6F7FCOJaSrESami8oCWVL07Z8UBObqwBLGUknK2wNxkv8wTcwl5eRTpXNBThblF3KlDkwHPnx96IssXGXHFgUND0OJV8GdA5goce+BeVt92tXLQSxLSM+CUoE7AlU2wZwY2l2FHVu14d14N7W44+Cyi/TKHJpTz59hZdFcXPm2CTHXTdRuoOm4gX85z/56nuGHjJipeD/5EgXZPiMFTp3BJqXxjB4rx/8lMyEsXp1QiMTqipv3UqbNf1ASaENUon1SpAb9XVRNXqqCdalTtLLEcxZ1B+dIkdi9FNKFYVi73xH45Lv2SPzlfBc/VhSzKMWwDgrZiKXwpV6qgEodFOZcdy1Uwa2rom5xWvYhKtgowCEtx0e4eR3TFYNRWYXFNoDUGCS6rwR0oYBw5efFrSgekhZQWfp/A8CissiM1pHRRyhXwocz49rp50RWfwdPbnsCSks1XLiVMiUI8zvYToxgeg4ar1ym7/zwyi0m70GsvWYLMUYi+nOJIHNtBOg7SkUjbwckWlDk2y7H+0nCvv5LUrGskvKZpbj0azHF+/Zpl4c9JoMylGCob6eHCE+ZF+UxhFIOqJaFRVx2cFth55Yw4wHhJoS63XAEHhrAnpinlZ+buq1iBDj/sTcOzWWQiANeshfv2Qt7E9+BzvHPjRu78oz+gp77ropcbPvYk4wd/gdcTYuzwATZcvwEASQXdZVAqlwkDJU2tmzOSNsnOTJAqplm3cjXP/eA7PNBb5u43vZ72a5fzw90nmNp1eO7982g8z2ewXdDNFBCKhMmls+fNm4gIyDQ0bawnmchQHv2fbLjyvyszRyfmJnMWCiF4UYDn1yELPyd8qBMijlKQWdaK88ksIKofVRftBT63CNZUuXcWEiafLwIVnTt2AIwkenmE2+5cguHWYbUXBoYV9VdtVDHHn0rAk0Ow6npoXcQVr30V//T+z3HnNa9l2ZJ1Fy2dmDjdixg9TPHkdqzCCOFYFNAwDIFuGFQcE58H6n2CdPHshTzw7ClOHzlNY3Mn0g4xlrK4eWM7d21dyvADj52NB5wXyDvf3VRxn2fYivw+L2tWLOfTn/0sv3j4IdpWLDkPAyvIalZgct805YHy2fk6V5UZZFbqUEyNF5PLfVb/mzJLAjFrVs7brPSGXx+sFC5HkZYxp+XleZ883/EZZ46DNgbco8PXB2GsAovE5bfDbqmBN90Bv/kO+PoXEB9/HSdKWWzbhuNlaGmGv/g0rh98W7WLKZvKVg6akJxG7xtHFwJxkVUhAVs6zJSLHM/E6U3H0WvcFKo9XIVm4nG7qQno2AIGM5LcOX6wdByS5TSxznVcf+MNBHwuBg4cpycU4oYtPRe99vnEI9TaD7k0/u0P/x87nn+ez3/2T7hi9Tre9bsfwxs7D9TJmb2X6r81oNVQbRY+uhJuq5177wwXpxLVUSwm1W4j/+fFEWjtQXXfkrlNpAIt6ztp3bT4Ih/+1WThajqEan+2D/TlOhKJc8C5MA1MDSqSF9JhpwN9Eu4MIlIOcvASkZj5WXKBYoP8zv3w4VfB3lPIrha0rAspBPQE4FXLYM8OzKtd0OxRJ+H0OKQnoGKTyeToL04TdblJ57IUkmmK0iFlFajkMsykkwxOjFEKeXj6uQcoDM0QkAVuWdXKMrzUSkGxUMCISApWkWBM8RtEu2FmgjnHWcLx3v3cddPtbHjX7xH56TP8ePtRPjCR5y2vfh333buHiuWwUIh1Ebix0897rqylPLof27bRhOCh557hi5/6Y0rJczx2TVQr6XRFnKCBePda5Cuj8Fe7Ye8YbA3Cw5wpEHK/qgHraBI5bqnAzXxpNdSJf8KE8WpflP/L4kjkVHFuPc7erg5j2/q57f/djW7qDB24hJ/8EmThihRBHZ1BsA/YczvUhQIkM6jdsB+YluqzD+WQNqqxZwOK1W82VDlLCt0GrDPgGQemHOg0qv0KS7D/Gfj4bXBgH75AN0KXeD98I8WtV0JxkeooFU0r0/O1YTisSEn27d3L1VddRXiVm3w6T/fKejau20hTuI52oeGR0OMq43GF8LU24aqvY3mrH2usF19JDVCWTTQhsOwSi2p0emdsPCVeFH36lz//Z048uINt+w8zMDrN5NQM+3e/wPWbN7FxwzJOB/1MHzyNE1c+ntA1lizr5tTxPpx51XnXtHn409fX0d1gMlGUfHN3L5EnnqaupYsDO09gFs8x+kMG/t/eTGGwXzGA/GQKNoXhtlXw109AbxHe0aGcuy4XnFKrrbJvhtira/EMuZh46Jw8zJAF8dyvxhQ5K7+OuvJzRaKwf+cR27TZ9d0nefeffJx//cQXKF4EEvVSZOGK9Aav4rldZMMzKMKAZhQ4df69z58wBxivKl0nSmF81fe3Acerd6ADr9UgKGFUws216jrfj8MKS/VOWu+F13ZBfgSWthNOh9DQKCemoS0M3/0RxPxwVQ30VFRpeb0DaZB9JlPH+pnKQF1PmD/80PtYZiwiRhiv9CAMA4mB7g5w86pb0Nz1uASMPPvfuISaIpfbwHDraOhc1d5Af2qKI4at5mB8bvgj49N8/f6Hzvy7aNr89307+MuV67nnDa/lH3/yOLf87qd59LN/ANJB9/v4s7//e+791jf5/nd/cuZzt22uY90KHWfGppYGdh46yX+99c1omo5jOZjWOYqUtSi8cBxCFryQU3M+VEK+/0dQsjHWtiI8Ucydp6EbZWFUgHEL84ksyYELaMvLFa3/n4pIe5jD481KNQARH57iwEPbed27f4Pv/dN/LtgyWIgsXJFKEdgbVyx8s12hzr1hzvm3ALoEdFY7fZoaLNVUv/ohB9ZQ7V6Fqom+1g/7CrC0AWITUGeo1o93eWHtMtWw+dQRfF2b8ee9SCSegIdK727sxH5YvhU23AU7Hoe/PqiISXTmWEME1G9qY0vXB6nRahFiln1lzgFwz7v9xs2vJj+4G5C4XDZuQ4dKhV/snOZAwia0WSBTYM3WVV8gX/rzp47ykbfGueaKpXzlh7/g+C/vBccmctctlKemeXDHbope31mb0DNjWTYVWnFXPCTzHk5OFzCtS6zG7Um1kDLVIUX0KgmMxC6WEQfi8MwUNHFm8xMecIX1C5ebSlQY/deR97lcOR9Jw7lyiajwtl88xu/9xR+xZO0qeg8cPvvFhXz/BWThinR0Co5Lteh1lPmUql7YgzLXxlFmgBf1sIKotmr1Ljgp4aYW1eFpNKH6bqQ1uL5ZJV3LFYg48NbV4AqCNGBJOzzTD81CxXSnxsBTxJ4e4OBgHMeW2GNDOBMTivZrxWKQIciMq4WSr97LaqEKDF1u3MvacWl1CHHpYgrH1ig7AonEKuYQQlIbECxqbaN7WZQtr11JquKQmdDIzaTZ+fw+dve/GM4xlS3w9W/8mE985i3ccEUd//GfTwJgnT5BZTDBz8e/jlkuV5VIKfWuwwV2rPIQrDd4aEc/+UspESi/8gyaA1juggF1pMhT04hIVpWymC70W8NYjyaQhkZy70XMHANo0+H4/0Ji6Fx5GQ4Qq2zxnX//Gh/86If58z/4MyrFeZr3K3z/whVpMfC2Tnh+AqZKaqH2oXwdC7hKQEKDcQ1qHVgVhppalUPyGdCQgyYf5AuglWBJEO7LwXQcOkPQNwO6A2YBoo2wuAtycXAZioZrUQ10XgPfvo9STQqXN4AQUDk8oHimXr0SrP1gBsFTgFei2mguQ5FAJzW4fQm4Z7kcLi2a1DBsAyQUMxkkgo6r7uKrj/8R7mAthlu1ppIIpONwfMezXHfbHeTK5RfxuXxr+37e+vYb+M1r1nHDhluo7d6A2+NQmhojPzVKoawxknZxfKfBvp0JetP38i/fP44vpDE9fTlOigewFcjz8YKiIvMLsAu4OvyYgxXo8eNOGlhCUU1rYR3XdSEqv8ziZM5WGHerC0+Tn+zxi4X3/v8lo6eHyRVSbL3tRp6476GzX5wlmLhMpVq4Ii3SobFqfg2VFDfAJqAgVHOdVWF1dV9UMbNbLqitUew3fh+UpiE3rqorLaH8Ic2B3Sm4xg/dOhhBeH4U1ntgckrxMYQ1hWeLxWDyBIgCLrePrrVXMBjcie21wAmA9CqYUEiDK+uhxoJIGZp9MGTCjAduuY7glI53obFc4UaTLkBiS4HwBKldcSfnZg0EqifPsg0bWbFsKQcOHnoRADpVqPDQL57mw6+7irYlS2i8+pVo81iMpAODj8HOw7CxXvKA+TG2579FpvBD4BCXcjLCnjXc2PFREhOLSFUyNPlN2pYI6mMB4jLOshtydG9twvxAicJImUDYIDuZwLLy5BqyeBfFONZyjMMv9KOhrGLLpbGqI8rBoXGKq3OQK0C+iFU2se0KBdtmoGJRcpwLr7sgc02WFjLlukBeRvnISxEp4Sv/8BXe+bG3cXBnDfHxubLJYDTC0nXL2ffkzssqFFy4IgX9qjYo5lY11sJSDOx+HwhDdSCXLlXibRmQzitkZykBRgSsDJSKyuRI2jCYhR7UmpwoqFOttR7WNihuqOQMfDOjEN/LTIj2Qj4HHkGlVuOFDol8ZzPsHIY1bbBzEm5aDCcmwN8I4SLMTILmUr6W18DwNSCMy6A/1F04mvKj/LFGhNfLxVJvWjDExq6lPHfw0HnXzXPHxvjA6wS7Hv460ZkC19/1LoRQhCPDD8E/vwuG42AisGhjg/YZxrW3M2J9DZuvohzU84tLNrPGeS/lsoFPgL8ErkHYGJVc93XQmqkmoqt3dvYvJVvki/xvAUinAsUCFMrIUhm7YuE4FiXbZuf9P2bbN75NX98ocVMw4hTJI0lQtTKrvtXszM2erT50iufJ6He2LWI6HieXn3PKPECtS4HADaE2HSHO0E6cce8sLt0iZlZSiTRP//QR/uBjb+czn/1XypYiU3ZMwSd+55N8ZviPGDzVy0Ktl4UrkstU/QOTWcVIWOcFTHVa1IQhV6liZhyYGFDFeHoOKmWwUjCTUT3oF3WoZqW6hGhO9RRJ5FTmMRqEnm7oPwZOEPqyqhchQFMJurshP4xW24AM1uE0WrBZgx2n1RPqGoGJJLgKMJaFsAuOZWGlCwIG1uAR7FI98kyC6uIihVSmJQ6To9M0lq1LYrc+97d/zXOH97Or78XlGieHppmYmcIOWHzr6//MuqtfSbS+hUoe/v0vYFtcuZ0xFGdExgG300GH748Yq9xO2X4K1ZjxPpRSzUUITNNhIKPQUj5UbCVmQ+3doqpEs+8U5/t15rXzgT6E5oWQVx1TzC0tj5Qkv+FjW+8IBbPMDCrrYfPi1GLpnGuVLrDcB4YGzzoJoi6dv9tqcMeVFUplidsLsqSMnoKllKpsAhUV+8pWFIFmJV9VOlvRdSDU8qyUlZFkGWDp/dzh24XxyvX8zWOjjJSnKGRN/uvLx4hFvswgg8DFIWWzsnBFkiXVhUq3q530AlDMqW3CLcDMq97yVgL8ZcBSqOxl3TA8BpjVZjz1EIpAMqX4bTdcBQf3gLRgqE+Vi8/kQXfDLe2q3mcwB8mKamravgQnlcd8Ypsiv67zwGgRtgFjO2BCg9UupewrdWh0qdyJ2wWPPk46spzizWU8C0Bzat4AhltNkWZ4VHuDi4gQglhPN+uuuuq8ijSWK3N6OoHbLuGSZRLT40TrWziwE/5tz2zVrUq9zS46CYiiC42rgatRJt4fAk8BvwCeAHKY4jfYmdcpoB5qI7A5Bi2vOC+S6GURcxJ+8qMKOfMKkvRV0TkRTHK4yGNRwjnT5W3W/JPz/oZzQ2XzlUhH8Jd33Mm7bt+N4Z2XY9Cqg6wGg7FR4VYfcx2VZ4GKLuaY+2fZ+63q56WknCkzOgOTldkq5hjbftmOzVVo2vVIubDJW7giaR5wTFWEZTgQ9qsM+kxO9VL0ByHkV6TPllcNJGurHdzQwRMBjwtGxxXbu8tQ/54YA+mH+0+rkotlLpiyQTNhUydMp8DIwmMZOK1D/Rgs76YyOgZd9WhrG3D++hl1whWBJkdFBqMhWNkKByfg9i3w5D7YkSJfV6CMyYJg0cKoPmObYLQGzV3dks88+PNt34JPffQjFFOTfPfBx84ylSq25Gs/PcBbrllMg7+FH33vB+iRXaC9kRI1Z33ffAvrbGywhkpevRl4IzAIjFF0rqK3MPf5QVQO9jMX1/2XLFLCkz+Ho4P30MdRoIDFGF7CgBcHEw+NOHiwsLDR4Uwv+FMoQGaQOULjWYXSUTUzedY0NvOWO5egNxwCv1/1n7Id0CzIyjlAalCourRcdXrCQtHMOqhAl+OoWijpqDSL7gWvC1lu4OcP1/KPLzyOKQVwFZr4Am5xLUVHoyUGiQsQ9JwrlwERMqEnAm216mQSOhQKqp1ka4PqnVgbgulpyGZVn5CKgKFhNYGtLapV9uC0Kicwgf3TsDGrWiDMOBBwYEsd3NkC+47Ao/tgcVjxMWzyqj6K8bwyA/dNQ18WpzgJbUL1py2gkrcnLNhQhkNJ2JGC4jHwWtBsYE2WsS2ThVAJOXYBCweEpHPTFgrx0+iBRZQLSYKxOlzuyHm3+yVXXs2n/+rv+MljV1Iqn504ffzoJCLgY8XiVfzNF75IyXLw+X+J43wRqGduYcHFzU+BWnTd1Z8Xi+lIUjlo/XUA5SQ8uQuOOCOoU3EacMgxxaxSWMTn3nzWGQtzfcVnj5dZrZhtimmxpfMKgq1JKPmQIgpGBSuZA9vGcOkITVcojoymbDh0MAUUqtuOywWmq1q+7lHczsIPrjCUNEy5lO9v20/ZcVBn+Btw5DWUpPKDR+Yo2C8pC1ekLi+0RcEuKUMzVy0N7q9Ae7M6sWYq4HjUTp41VXVrKqvgPVNJmJhRAxudUYQWZUeRN9bp0Fp9Fgd7YTgF3fUwU4CTZRX+rthwczPYLth/WoFf68IgXPCh62ByOyQchQds9MPqRtX9d9qCh/vhVje4HErpDGaptCBFQjMUfk1mGDv0NEbtlfzDH/4+/af7efObb6V2xTUs6lxHS+MidM2DrutomoYmNLq7unnFjddz/8OPnvWVpi15dNcgT+wdoWwpH6dYeADYg8f9ejZv+gKYPiplQSoNfUNOladCcPbODedXtLn/y+UTfP2bkr/5fB3aeUgofxWRQNaVRnljjcx1hJ19df7vc/+e/2+bufP27GzqWCHIQDpNu6eMmZe4PB5mUkUKBahdFMLlDeEKBRBlEyFMEBXVocQAzRBKkUBRE1hlhGlVraUSsuymWA6xd2oStRgkagGWkfgvez4WrEiGrwHiEvvYDHRGVQOa6SJyyMLYPop7+WJKHg9yZBJR8uLsKYBRAb8LoUk4moQRS0FY8kLV5ecFIuuFrIOoeHAMB/dBL6LTR7izk0omTTlsYz6YwHt9DeKnQ4j2dsSKG+m8sgEZgOETu7GHKxQCHqiA3ubBfDZLMFpBjOTIjAmi10VZtHo5J/buJeBx4V4g6F3TXBgeL47jYucLh3j1hz7CO99yM2//yF/ymb/4Ju1dPyRRDNDduhyXx6FuQzcdG1awZdXV1Aej/O4Xfo/x5Dgz8TTDw+NYVe4By3YU3fIZsYFRdO27fPg9r+Tm6+7EKkP/SZvf+tRORuNDFAq1lJ06pLRRVD0GKiwx30z1oU60qsMgBV/76qNcfd0buOs2N2795VMmIWDVqiya9iMcZ1YZXl5A3eH0aZ7KLKfdK9D9ITyWhrcuQHImw2DBg511WNwQwys0MpkEkdpacrk8TqVCwO/GERU0TZ1WQbcLj6ZTcSS6FsAs6aTw4BBBmZIlYA8qmLOEy4W7C7nAYPl9EzsxdTeZShqh2bjtMo5tUiykiUXraPLVkchPY0sTqXkYnB7Edmt4I2ECrjBaRRIhjG2XwSohbAfD8uAKB3EcG6lplK0yAb8PR0qCoQggyBUKUKjg8WgICY7hRmoGbYFaTFGmWM7hOFBM5nHjwuNzkx2fIlYbxGVJRgZ6iTVGWNqzhYGxUzjSw4ZFV2CIS+8hlWwcM7kfX+sanvrah7nmLV/i0C//gbf+1r8ykKjQeF0db/3c7/DGDW9jNDFE2hBYHg3p1pFCw28JWvUIeq7En//lF3jkq9+7ZD5l3bqNfO0nP6Hs85FPW1QsSGU14mmN40WHZwbzHHkGZG9BmTG2Dpal+qlYLtWNw6gHrQEQUNhD89IKf/LNLWxa5catg9e28QFel4FL6OhouNAwqnyxC11CyRmL73+7l5HBHFKHfDlOPJtgaGSE557ch23uQEUDEryUklnNCHHD3e+kpmYH4ZoJYn43QVEhFghQE43idfmp90Zor63DsW0sO08ylUYgsUp5pFOhXCyRzWZwuw1cbh9oZWzHS7ZskNe6+Jf/mmT//uerVwwAbwf+Hwqqo+KTUl56rSxYkZZ9+Q4iNU1o6RIhV5BQOIinJoqWK+F2e/A012LFJ/F4PQggFU9jlkrocRNpldEcoeqz67wwlKVs2CTLFsWYTc5rI/0WpUoe0y5TyObRwz4I+LESRaLXXkn6J8/jDKaRa1yIWBB9R4FAvRePBwyfh/ARHzXuEP6ogTYG9dc1seU3rmXXz19gpm8Sd8CLrz5MVPfwR2/8DF7j0se3U0zjZA+h1S5h17c+yro3/AMnHv5nTpya5lN/8wOGayxcf7gOd10jdsmNY1hIw8LRKkiPD7HnJO7FK3E/P8Sd7/hTfnr3ByieHr/oNYUQ1LxjBZWlRWR/FpYbyOVbkIkKVnIEu96HPS1hKAcrmsAIwBN56ApBxAvHh2HNWmhcDcUo3HcQ2gO4lqTxLCqhzQwgptLoow7BG9qJ4MPfWyFSCRNav4aW1avJPr+TuqgLr1+jpqWN1lItvRPHMTtq0PwBdJlHT5XwxToRsTZO7TzO+LZ9aJ4g1nSGjLOI7T86Snn6fhBJEKaK6BbHlH+twZyZSrV4qlrzJKUypx0VLDA8dWhR0BpTuPw2/madpuYQS5e2s7ynh57aNla0ddEcaMWQHmzHYSo7gksaNIW6KeQypAvDIHRKosjRU4+xY99epsslCoU6kn1L2f3L7ThnQMBB1Il0JQpZ7UbKT15yrSzYtDupPwE5DU6VYGk9jJUV+FRoMFWG214Bjz8HV3WpFivjM5BIqLJ0n63QDyPABh2mbagVsKkbRAkKWWUqHivAEpcKhU9rMGXAyhbixx8FJsE1rRzLgw4cA44C16FSKrsACe6lAjspCeyNsD31NAf+dC8y64AG7mtDdN/Sw4ezH6Q1dmlFEi4XergZiUbQF0Pg5itffZCf7eojWzTpuKsL2dRCbiqNLvyU2pvJTw8hcpO4J5Nc+4Z3kZcakeYtLKsNc/MbX8MDf/2Vi15TSkliZz9IH3Q6oBehZlLBq356FGY0BUTNAH0nYbEPyiU4pSmM4jNJ2FqEHgklHV49Dbt6MZN5zEYbmgTkklBKM7Nrn0KifGUKQiF8ty4jFPKQKmeofPU/0O7qRG7PIe7rx8lV4Dcb1ZJpsuHpabi2Ha69AQ4+DEdnoLkL0GD1UtjYAgebwelQhZlRCU9tg8FJuLFHdX0eTUJDDVhFcLuhuwWyGYj44NQo/GQU6+5uRFgiTx6nNF4h2xJl0uvj0J7TeB86TWxdC+2ykZa2RdQEWtD8Ou7lAUwpCU5msUsFsnoWkSyQ9eQ50T/JmO0jbRZhMoFRcxDRrakwpwmQA2cfiMO0dmwlMTkOvIyKRNhQsKCMhOXVDgqZtPLTOkrwlW3gFOGN66r8cynApULl2Cogs6JavWrZ0KpBUxBmbOhYpGAk8UHV48jvgocqyo9tK8N3B+FaAYsFJMRcKXEO2MlZtkhlUqW7M4fT7P/93XN8e25w6g3G9o5RKBaVe3EJUY6rB4mkVCojhM7tV1/JG97yQWpXrae+oxERC1MolTAMN4VKmWxmHMvOEvBE6WpZStYcJ92YY+DwIa6+8xae+/J3mZnPEns+SWgQXg1aAY70QWsORAEeBHo0BYNKOzDsqM1nuQsyFuxNqsDf8DiU7lcA3lXXw3gInj2qXKtX1kHEUgWAP6uAa0otoJYs5YOPUBnII0NRaAgjQm7ks9M43pICGAcsKCahT1NI/OuWgTYFGyKwfQqsYVgp1Oa4sh20CrgcsCZBK8JrOuCFLNxRp1AqAxLKcVhco+Bdrjg0tcMzB2CVhF4TbisgNQHXLYbdE9DRDLfcjvPAdykMzGDX6qzpvI4Hf/5jSvE82sowcjCFrIso1qJcQtEUDCfg9jXQbEGbX9ESnIpTdgcgEFcBsiOWis5boI+7+LP//ixiynORBzUnl1HIXqN2wp4YLL8KHn9UTdSmtbCrFybHYD3Qf1DRIT2cgiYNWnVVmBcAujWYMSBdVnmAU3Hoz4Kehqsa4Oot8FcvQKNCKTNmQ+84rNZUqbRpQL8JUii+gWWyqoCo1EoZZY7PRlEr1Z9FGv7PrqDwpRNYea/KKSxEhAa6CY6BLxjC8Ph49R98EeEJnc37MB/tUHN2JjzqidHmlqy4dj3SkfzFpz7Fp/7s82TPCYvPl7B/M2HXX5G3gIZJ8tum0ZcEMeufV4SYqdM45HFO98NyHaYd8DdBU0bl+sJLFEZoeBiRdqHHbscKeyA6hEilkZ4AJPPoq+uwH59GXOvBtSxE5YG9eH77ThxvGLl4KXYph+gKQI0HdkyqerSgDvttlacb05SPboZhuRseyYEnAGYCGqegtUbFyNvbYM1ysKfhXTfB4Eko5qGlTgX7+ifAa0OrF/wWNJoQDsK1DsRPQckHXgdWNUFtDPY8DsU4LPdhHp5ktGcS+dHl8PRzOM4UBF2KqLKuAR4Ygo214Gig5eDwJLxhA+w+AnpZwR6ub4H7R9QG3QmcBru2QN/xp/jtt39kQUtl4YrkroXePljfAGMn4WBCobW74lApwpU+2FmCm0Jw7zCMS2XShSR0+tQupZtQNJQp1u/AT8YUWPXnFrynBMdOwTZbIcqXVgdVkSqsndIh6Kgd41oXhNywowwHTVXSca0XDluw3Zpr8GygSt4jknKwBG+JwmMWYqGKhAtVD1LEkTpS6OgLaAlzrggh0HU36PD+T3yKfCrDp//+77EuQKxfU7qH9h1XcnpGJeF1GyJ7wCq9CS0niMVs8o5NqT5LKStxMhaRDi+ZShHhN3FPh3AmZhBdBs0ZL42+GOHlV7Ns5RjRLjdSZknctZdoNMwLcjtai05ZFwxlMjT2TjJR6sPftQ7bn8fzm9czsv0ghVeM4fTFKc4UcC0JoDcvw9ppkcHEcRnYB9ywvwBhHZZ6VHdol6VOnjUdYCRh6BgsbldQo6kEhL2QqoCnDOkitAfgueehJ6hyP6cMSEl4MgmbvfCGoAquPNWror+5NE6dm0PmLoUNWhXEaO3EOtwPfi84FajXIZtS/nm6AjW2Mm1rG6B/EOIVlXppQFU4zDIF1wu2D+7i4wuk81q4Is2koGcR1NbDqV5Vxu13w6NH4WQRXtcNR/rhOwOwF7gC5cPMOOCvQEDCc0Xl2wyhTJCIATe2wvcG4MdFBWvyogg58m64oxG+OqxqiVLWXDVtraNyRfG8UpRNKL+wUr2mD1W52wTcDRyX2N/uA58gfE0P3sAClUHMTpEbbzB8UfahhYrhdvNbf/In7Ni3j58+9th53uEhUuhhYrdAd+awa+l+zpC3jGIgMHCE5wxpTk4HS8bQXcAjYHhq0D3Q4IPf/bzkhs/ejKZRTSBL2PReAJw7VDRNShspLYTmwXZM0AQ2FhUciotLmJUCUloUimVcPo+6voR4cYZpy812cZQfjg1QmtHw5kqMP70X16YQ5ZrVyOeOIAMCkTCRM6MgioiGNTjPDSBcXvQbr8J6bi8cTEJfST3/NJB2owcM9JuDsDSId81KSjMT2IssZNSHcyINXgt9tA897YJygAY9QCEdIrVrGLG2A9sG0dKEjJdg7WYIPa+inG6hdqiKA6MluMatOsftlUqJ27wcObiXeLnEQgiIFq5IUT+c6Ien+qAtpEyM6aJCE0wCWR1iAdifVqbWHtQibxFwwlYnzE7U73FUumGfCVvSyhdKo8yE2dzeyQp8aVQpxurqa2XgHgPcGhyIwwtSIchbBEyXFdPqXSjfKQy0emCtG9ZYsKMInTp6Vy2adjk0RgZgY7gWXHxxSfEGAvzl5/+cXXv2MJxMnn017Ro0bSuWozbuCsqdq6CyQyXUXqMDs+0lG1BWt4FyOSMCokWV7L/+Zth6h0A/a8jiDCJDP0NaOZehNua1BfUD0XDkgmPpkV1kTsES72quuhPGKyW+uv0xJgaO4fyiH8MuIaWNJmx8RpCyVkYKHY8/Q2amiKFZ+O8bppA0EVLHZQQpPlLG4wlAzstau4W6cIp2e4IN4QeQMYfSGpOyVabg+HBSQcI1HbRsuAOfaKW5Zh3udTojE8PYYZ1DkzuY9hznsd1PMuN5AikKlDPTWFMO7qRD8QWwVkpEOYzpsiBkwxNZmCwwM1TmwWdPsPK29ks+04Ur0ok+eLSinlxfBbqqUfPVMTiQgUdOwQMWXK+pMvIk6uQZlurJN7igRqoWlFkUcWQF+I+k2vk3ob47iYpTXA8856gnuQblaN9igEtTTvb6ajl6AThW3UVa3KpWaqkG17tUYMRtgahArQKwzjx9kvwdWYjWcmmZVZ0yxWLmZaWkWrJ5C3/5mc/yoT/8NHnzDL8ztvPXHMwEzmoAn0DtO7PwT23eaxqqg00INRVlICIh7kDMgDs+BO7wy3ff50rmFHz9bbAoJHB88HdP7GKw+K/AblzYCAQOZRwkaUBiI7EpIgGDCnkqk2lmE7mzM1GoEsfvHp7hXT3N/PEb3NRNzGCUdfBJpMdAhOqh65U46z+KFlwGwqWsBilZX7cJ7BleGy1SGhlg8uZFZEUay9IpRrKYPoGvWSPXAWO5KN/7us4v96VVLVQW1V2DRr74W2k+0XvpebgM0Cpq4YdRgz6NYhbanVVRuKPAMg3GHIW0WIpSiDNJY1MFI0ZRKyBZ/X8XcLMHlnlgW0Z9b6OAqK4gSA5qhYRA3NaFvH8IWi3YGVemYDUiS6sAo6RMysWaykN4dNhbhrUBuKsNZBDGJi6b9EJoHrqv/5iqbUKCNJGYOFJgWjYuw4uu6ZyVG7nUdGoab/noh6irb+Uv//0Btu/0A7+DZAnz69okLy5JODe1WUFN9azkqh/0CJj2LPiWLlukA/vuhxeOw7MGpDQYLtYj6MZFAj8SF4IyWTR0ShQpkwYK6HjRiWCRBgwcSujo6DgIBBVAw2RzU4gv/OkbqKt5CsSEsoRKAlGuh667oee30D01Z2MehUBkT0P/P4C7F59WoLMzBm5vFcIWUj5ZOoss17Jz5zr27XgMWZidaTdwK/AHjJ3auKC5WLgipQ1YLxWn9rCjrqW5oLMFnhlUivQaPzySV6HVPGoFzBKlDKBOpoSArS51ShyofvexsjLlZlCVAiclnLLgFahTLQE0gfzeIByuwDqgxVY2zwxKOYOOYg1dbUDBBaeLir9tEdBiQCUOdhlXnQ/dvbCQ5qxIM8WDX/kCgXCIuN7IxNhRfuN9b+S5kzvYdbwX6aqhtbWdhvomaiNhvO4APleQoCeMW3gJej14DBeGMDBwgXAp89JrcOu73oB/0au441VPUCi2MMezO18pX5omVCzon1L7xq+jlCI7Jfn0F0vsyfrUiSnAIQxMUeF0tdxeokIms+dnEXUupbGpACU0PICJpBWTLOBVFNEI3nLTbdRu1KCmRnWv8NUrIh5nPdS/XwXBXiQOTD0I7j7wJVUuznEpYLVlKmWSUYiGccrr+Ifv72e8UEEZzAIVAv4gajEubOIWrkiPFuDOqErGhbMKgf2oCZlhdQLVoZw3U8KsSWkxRxubAJ5EVc3mTeit/v+1KIXyyWootXrvk6gE7j0GDFvq8zsr6vVVAh6UCvScQ5l+3X4YLsIhS/GxnUA9w3HgZEp1lNs/Tejua/B6gwseNoCUJu21OVqufRt/+OkP8e1fHOLL3/opJSwmp3PYHh1jXRjPxkZ8Ta3YlTIur49AXRPa9AwR26YmGibsCeF2+3A7HuoiAcygTiwVxhvfjB58AIpfAzagwkh+1M4TQh39XqrmwLy/BXNdvWaTa7OocBdS5tj2XJL3vrcb49egSfkyDKWMufJCCSrC815UefzJcz4xH3ALs/WyTpXzy2HwrHebCKTRjn3wcfTIEQhqiHIcK+1DW3wTWkvTOd8vwSmAEDjZQwgzicjPqKly8oq/wmxQ0WY0KGpk003sH86gIlQ+1HxvBa7icjawhSvSJgPGM/CIo04KG1gBtGsqlP3WOmiNwjOnYbOjTokksNaAAUsphUBl5KelCjVOQrWURSnDDHAEtYZKKAVN6nDahhNVRWvR4S1L4WPHVG3bq4WKuPSVYMKp4jg1OOnAJ2uhWFJICq8P/GVm9g2Qf2UCYpfhONgmVjZPQ1stb3rlUr5930F6TyXUeFb6YEkY++6llNPjZGI5yBUheVolsXv3q8GUdQiEoHURjCfRhgo4sQDaQAnP89+g3F4HHIfyk6r8pGRyhsgEb/XHjWJzWV/93YpaJQXgEGhrwXEz14aiwv0/u5f73vUebr62Fj8mjqwghYGh6Ti2DUKga3rVl5HoCLQFllf7QlDbMs34iVrmiMw0FHXUq6oPtFi9P8FclZ1kIdg7CRzN6+zNxWikidqyju7oZMpuDBmh5nwk1GYC3A0UTBeelI1uCGQ6i2ZlkeEYlKLgi6t2qFaQyWGLZN6q3mMR5aXNFjYtXBauSBGpOBnay6pQ78Yu2DUJLhsWORC1lTLf1QDLfbBvDJyyCjOu1yFuw81eeKKkTqph1O8Syg86Ur2bLCqU3Vt9/ZnyHM0XqHKKb/fNIfb3SXXkN6I2Z48Bhq2CF9NZtf52VmCgotAYqVy1h89liGVDyca2C4wMDM3tqZs0WFaB7hz4xlW9VP8QsTd9iMzYSexjJ2FRCNoWQe+Iqq3ySIye9VgTw5BP4Ow7TXG4BD4NXtcJb3gPPHMEHj8Eje0qB6I3qAfvCqgitumfIQJR5MgoRBuUY9x3GmQdEFM1OoSBOnK5cT7wrvvZ+K67aF3vImtPIiIN1AcbKMdTRLwRYsFaDE3DsUxcukHM5UfTBH6/Hy8u3K4A0mPi0sBX8uLyaGSdIrpZR8PqMTjxH8DNqMRfEbVr1qFOUonKxs+SIM4qU5WW4KywimS2gnY2tLL95FFqhq6gOVJBL0zTWhMlEIwg+o9ydU8Kv38+REWApx1wkG13cHz0KG7dhV3S8QVjWFM6lN04QQ2f5qNYNOgtuTBCYSjNRk+LqBzNLhQV1ctt2mWFKiu/sxYaPNASVY79wSl4YzO01yqClI52hWRwSkBSLeq8Bl023NoIU8PgdtSiypXgtKlOmzAqjptFKZlEbchFoaoaZxH6KeB0RSlbe/W5nHDUs7vVA6/oBPs0rPYpCJLbC+lxGHGovXkN4kAGYV+emSMdnZJjUCjB33z3pAoGCBTOrV2HnnpYsUI5sFu6qfV1kB/Zi10xYOVmRT/WWoDBKVgWhdYOmD6FiAWQK31wTSckinB6Cnr6YHwX1OfAlVKVvzVZ6C2rDX7DEjiSQ97aBsVuiLZCqA5ONEPTUoVh6x+C2lowXBBvZuZgH49GjygymNNHIO4gtFpE2yLcS1fhjBYQQ6O42jsQfjeks+g+D7rhpjbYgJ2QmLXg0Qv4ymEsx6boylJO+AneuAn9WAL72H+gbG0NIQRC8+LwXgXTEfVqEyYNtKCUSgfcoO0HZxS4Qf2fBjgjqEjSIMeP34fr0OsJLC/jZowObwifqcNkmVPGL1i7fCMuIBTwYwgHQ7Ow7QSmKejXV5HPxRF2C+WZDB7dhbQieB0fPo+NXXEYtU0W99zI+PR3mFOafuDzqN17C2qxXVwWrkhLahRRSdmB5S0KZ+c2YbFfbTi5SeXgh2ugXITGEGyqKB67ggOrQhArwF0atMegsxWeHoJgWplj13lV4vaOoNphLQd6Syrp2x3BRRvmN/apMuIVpnoeM1IFMQJAUFOUxVoQFtfBiTysaoShceoWL+KDr3sfa1YuR1yTpLW28aJDPRcQb5tZCvmKItKoVP9TgK+5ldL1Dcj2bnjwGVjfg6drDROpE5gRL5GVdyH9QTKnH4ZSDqhAaRrr8P2QmEa/7nosVwFOj6jwfXcdDMQho6uE0NoG0KTChcUHFZXZsjA8fQIj72B1+GHiuHrevrxCD8xMKnhMbaMCvlbisDUDPg+E66F1KRw8iYxE0Zobsdqy2D4f8uhuyov8MDoEzUGwi+itXWRDUDn+GFx1G/TuBX02p5RSXRBnnoA3uOGYG0+3g1WIYx89iow2wfhRKJTB5QNHQ/i9yEwRUgVw+9HDi7CnR8DKgXNIBakKBRA2eOugXMIxhzn4xB8jBlvQV8d4rmgS1N3Yts7DpX34jh9FLxcIRP1oVhGXy8brs9FsE0PaaKaBIQy0okbAF8Rv1LOsfQNLmqNEfKCZfu7pWkrhn9dxcLsXWXFjmz8HHgHej1Kig5dUj8sArboUk8zgCCxvhPEpaKhTSIdcFioJ6K6FUC1EPHBiGAIekEXoDipOtHhcme/uEqQmwSyqIMBNdeqzxRk1ye2NcCoLm2rB0mHDFZg5Dc9rV1AZO4nsO6pOp0YvdDjQ5YGWVmiIwZOHFTXXrT2K9cjlYXXLFv7s1f8PTWjKN7uoOBx78Bs8vu0pZoo2n/n7f8I2p3FhoWkOvlnDTgf30gZK5YLiBWhvIdS9meyx/Zj1IWRxgnw2hN07AqUx9GVLwaVh/8tjyvx81TUEMjHywW6sSAnqIioIE2mHeJ/6d/NSGuuvJDlxmIprEvxFsAtob1hLqHktqaGnkAFDUZc1NkMyA7oFLheBoynK6zqwGkpw4JA66a9cok6Gzk5orsW24zCSJ3DFHRSWL0FOxsHR8S9eQ+Hw0zjxQezUUXjlCigMQVcj7OuD6QRc0QOFhCrlWOyG2AuYrRM4iRxscUP5uJr/gQJ0RCBTQtR6kIEI7DmFuG4demMX9kw7uj8GJ09juyQ8+kvYtBhWrFYdNaY7kIkcIu/F7qrBHB2i0jfD4g98lGknwYzuo3R0HybjSJQlJOpiYJYQpTIN7avJT8bJP/40oqsZzd+EePw5vK21uIaPIbw6Vq4Rp17H6kmqKu6GCKQbVW9bb3FB6rFwRbIsWL4a8MJQHPDAiXFgEmKNEGlSps3ojApAnJyBdEG1BcnYyn+TAtoaYDwLuTTUObC8GTZdAdt2QY0Dz+ThnhrVWSKeg5oojAxBxaY8kVAWwYplys9pblc9SSemwPYqwGRUgw4PHJlQfBJdnWhG7KK9kc4WgZEdYYl/jAf3HsYqJxDCpGzncOwSxdkKUBvST+6F6yNQKUFDDbmnfoJYvISAiFFITmFlx6C+BUyJs3sIWiLQEIL2HmjsoaC7sPYegeYQ+MK4pAdvzSqyhW2Ira9FNoVI7HsO22VCYyuEGyBvUNe5BrOYROaLigat4hBa3ENhYB92xyJa2zfwyZb38tcjX2H8me0Q6UA0NoOvFREO4ngqUEmDz4/o6iH//W9DTwdixSpkfJrCwGGoiyEH+xTJTZNfhY4dG+obwe0DT0zhIE/NQDELV23BGZuGmgisa4XjByASg5omyE9D0MJplmp9LAFZk6DSewg6lmCXe6E+C5EoXFUH5hSMv6DYpLpiuCMhGje+ipFt34HGMvK2dZwe+wXS8CNiNdCuIZITyFgAcnFk1AS/D3lqlKmnBhGbunFiefQbujBHJmHlIirBGji+DzavRXgCCoJ2vQGyGRGqgZYO5NGjiEXLX15F0ooGzkOPE7huK3VyA4OJwxAuoa/cip1OIqbSyOxpeLgPNghY06B2o6IOjhdaWsDlARzl+9x2PYzNwGAvyDBkPOBzQbcPoUfw37iB/M6dkEyCt6Ju1fZCS4M6fRI5RMJBTiRxb7wJKzFBqHE16eHtcMMmRXLx8D7YGiZdHCclbaIXikaJeYFZCaO6m+mgD+rcCMMLwo1tiWpCtqqQGnhqailnC4iNa5An+iDQgqcUZnH77ZxK6+TsslroMoxcuhZmRmDrBoXdMdKYlQziqi0qMeivQbZGyLumYG0N0h0AoxbL74KRYWhqgZblsL+PTEuQpd03Y/q6yMkxxHSCpmV3M5I1MTqbYHqae/UHmXrqIcSGLaB7Cfe8AUPzkJrcCR1rVa3SY48jO3SF0p5JIQ4cw+hZgojFqFjjCnHf1AVjE4j2DcjpYQjb0FkL6QRa0yKC9T0UTu3Dal4MM15EUxvy0F5oWAbuRuXjttahpyeR8TzOzw/CB69TVoxfh8IYpGagsRZ270TIIPK2a+DRp9W6KOmYVBhJPITMDoGVh2cHcSIBSGSQtS2wZwSu60TrWI4zJaG/H2yJcEVxNkSgrxeaoqCFwRyqUr5NKVN6cBxj7Tqs3hcgMwU+A9m8EeLTGMs70Zd2vbyK5N50PeXUIKVslvHxQYgEICdwppO4Vl2Bo/VjxyfgniWQm4CiA7WtgA8ae6CzHqZGFBlK12pFnN87Ag1+ONwHt1yp/K7mAjIQobHhWvr9fYoc5uQUbN0Kh7fB8Aiss9A330jAaCIz9GOF/PWEyf70h1BnIEYT1LzmLnJNbZR7D7NfjHHjY3/MSncdy7rWUJe2iExM0+D2UBupI5mZwR+oRXe5GSmM8587HmZmaIg2LYamRymWTOLpMuRNQrOwA6/At24xTkOQaONVmC3XURdqpZwc5+TOHyIWd+L2htCSBUrHd0FTjQLwdjTCxDSkTRBRWpbfQqUuS2L/C8h4L9KTVHyAJ3fAqShMDEIxDTVBOPYspFKUnE4OP/lFtKE0dESRukPv9/8S9/Ek3R//A44XH2Dy2BPY/WOQjcNAkeyV08juOqSYAWpgxERccR04M0iZgsXLVJmKlFRO7FQmeCqrKKM1r/JfTEeBPKMR3M3L8SXrEJoPrWsJXn89pcYxtBVrcfJZQv46fMs2MXnoPnCCSK8L+cKT8M7rEN1XIbc9AVdcAS6vMgPD7XBlE/qiLizLgk03QncXnD6ItAXoUdi4BkYGYcl6KFTQAlO4g/XoH30N+VwCRvNokQ4cIw9+D1pzJ7ZThEgbHDyOnS6Bvw4a69UGft1meGY35tEDsLoHPMvBMtE3XoV9aBe1m+9iaufDyh9/uRSp7KlHWlPYaRu7vgtt7Wac06chkcH88TfAVVJBAs2Alk4INcD2E3DjlURWbSUz/QIyPgkn4xAKqOI0v4RrboBdR+DAIDhpqA/Dvj5Gj31XFbaN59FWX41oWIRd36J2z3IZ+4H7yKxeDV21VFIDEI1CbQQMH1pGQrxMeXIEGkLIdddxcHSGgzvvp66ryJstwbrxQQI1jVglndzoOBMmGIEwetjFK5d3Itobaa2rwXC50TWJ4ZJI20TM9pQUguzUJHUbthLWGjllj2NbKaz9e2i88g4GHv0X9I7lWJ1daCvW4/QegeEsNJRxNyyjsncXuPKMtw/i7N4Fx46qwkkq0LZBLdxFHdDQAIk4oq0b2X8SJhLw5DM4a7pxprPw1FFYsxJaujC99cTNPO5KhGJdFFblFNYwN4rjlZCdViZdQz0MH0LmMoi6NrT1V+FM9OP87EH8b/lNXF1rKKVHoLMHZ3IGUSxAZjes2QTP7canXYV9cgC3J8D0xBFoisGzD0A+g10qgu4h68mTi+8DnxtXfQ++6GIypRxaQzOx6JUkViQVCDqeAo8LrTkEM0nCvgAzo3sRzV2w/yhMD8KqNciaFti1BzqWQNxCa+rA23oFpRMnEaN5AvXddHTUM5oaILZlK4NHf4bt96LFczilFGxeD/5WOJZGOG6YKuNpMrA8bdibNiD7x8BwE2hdjCyE0Zq2MrXnQaT3ZQ5/y1gHWqGIq2Mx5dNHkBkbXBFkUEdceRvy8H2KH7piqXzGjiOQTMCzj5M+tAMOZWC5oXjDb1wPp45BrB4t6SBbtuCvlCmm+3HMEvREKCdmYDQFN9+Ek09CfBLq6sBXB5lJqOtUgYnubnD7MJpXYtUcgpFT2OSYGdsDwwPgdhNaF0KP+sm2JHAnU7xv6TtY1uPDrXkQsoLMFzBLZbRACGGXsPJFZKmMdGtomoHb8OIWOnhclDRNNSywwWkIkAgUSDV5kSmdSjqO3d7KyLHH0a65DpHMoHl9WPksdLahb7wSe+9jOMk8rsVLsGdyNAZ7SLQm0OIZ2t78HkaOPEnZBtndpPJjEy7oasfVsoRKbCl0ZXHXtVM5fRjjtpuwr0lhCBf+2lYKQ0eZPv4kTmsUSgXVWzdYi1FyYzXWw9MH8dx0JXqgm8IqF2QLyIJEjk+A48e/+Gpsw0374luYTA+gGSGKjXGippdMbpDCkb1geSn2HsGVhdimDcTHkkjTgHA9/qSb1pVvoi+5DyOTQz92nGJyCrtNYDVY6A3dCE+A9NAxAlYtxco0bt1P3drrGHv2xzixKOmhZ9C7FqGP53Ce7yf62jciaoLE929HilpEbx5XOIgW0LGsPE60jVBTD63+BuzENOZknmlzGIYKRNu66Fz/Vvb3/QINDW1kBifv4sqld7K+7R6eMVKM+IbQJ7Lk3I2UB49jNS3FOtlPyBcl4G3GnBh+eRVJuHSc8SnKqQwkE8h0WuVDTg8gzQJiMIu+uB1r7zG4uhmyhuLBy2TASSmHPBuGegN32YuZFcj0FMJvQlsDkZwfy85Q7t+P0bMC68Rxxar5y4fhihUQTxAgiJnOUJFpxA2vQO87jsfRKOVS6Pt2IWrDNHdtpSnYjhas43n/fyD8IaLuRuo1P5lIgekT+wh31+EPt84NLnT2RKgc/WwIXGBbBdKFIlJo5IVQ4QbTQTs6iNX8PJZHIhq6qfc0kbbHyJbG0KSN5akoM1fY6B6BfWI7Yngcfc0VmIaO1rmIoC9CZMUrKPVsJKuXsZuaiPmaSMf7cOpDyGgYYUoqR5/D27GWSjhA5eR+wqu2YIYCaKEamkU9deFWTqeTJJMJtJpuDMumUsygnRhg/es/x9TofgLvuZXRmUFKQ0m8CZ3oqlcSzdqMxnTy8RGab3038cFdaLUSp3eEu9feTa82hanDrr2Pw/Q0AW8NzUtex+lf/jeDvU/iDUcoP/M8kZYeXvHmP6KARSCfJdIYphiagWAN6+vWUOO4GCPPtgMPsaFlGZvWbuS+wFMYkTpeHV3BM4EGRk8eYuPNdxIyfEwIi9ym97N7Yh+ujIsli1/J9AqT8tgM2rFjdNStRoxPU7bd1BfrWFG7lEpjK5WSQ6yhnUJkFR7hYo13OVYoT//gbm7fcg+/3P9T+nfuZCpdZDh9GmfFFSy/8hUY/ZNMNHSSGh2hNtrJa9ffzVE5Rs3SCguRBSuSz7IVuGAqDktbYPAYjBYg6ia4+XV4ixWuaLyaI9o/Mb64GXHDEuy+7VVg6RBcMapMm43XYsZTyIYOCIaxj+0F/yRjUoelnVDTgz2+S1VVFgVauJOaJddhamV8ws1Utg92PInc+xxb1r+Xq+rW8bPsdoJ50F1eXl97HfucUxwv9CLKkkD3cmaKOcpCkjv8PNGly/H5L1xfMydzR7rQPJRMG6npZ9Cfhs/gilfeTmHFFfTZGYqWg+GvoyxPwfQUPm+IomEgJycQixfjGhhDVHTCt9xNoK6bkfhBXE0rGJk6RqX3GI5dJrp4A1G7Fs22qZGLCGqLSOeO4G3rYEw7TCXZj6OBsbSB3JFHWLn6TQyPDWJ0hlksG6hpuYkD44KZEyY9m16F2z3ExLJFhI0ItZE1VEoFQpEo05Es3lYfW12LiLq8/DwwxljZwjHz2JpOqBikZslmTphZWlztPFM4RM0t7yYxtJv1vqW0hLpY8do/oKAJXDk4vLGRGzuuxdIMfMUUf9v+ep63xpEVjZwvyDInyM9Gd/EbnRupWfYaQv4AT1eGWNt5NfliChyT9zffwkDzjfgF+PQA64SXk3aR3+hezrbECUKxBobjozRe0cmStRV0RxB3TRBPF/ntRVvYrpfZ6Glny5JWVrqj7Ky3sbJJdu/ZyWc3bOWTuQyGx8MHNr2H7237Fsuufj0jj/4H/oYOrvR1MCiTLGnayG5pkMzneObYM7Q2tHFNfceC9GPBgKJC4QSsa4PlUZg5DM02RPIIe4rmSDu58UGmk6eQwiDS3oM9cwBqA2jCD6eHFL9DQxRycYJLNuItuiA3A844GAmoKSBm+mFZN1JOQ3IC1i1HdHZSKJYpVMrEK1mciSGVnxo5ze4Hvsgvj96P7Qkw7EohA2G+XnqO+0/8iN7RPWjrN1OM1VITXUvO7cZ649vJRyMUzNJChw1AKZumo6EerVzGaylIi8vv5rpVr+QVnW+kIbYRLS+oLwUxpifBrRPQowQCnTSFVuKcHsAUBtH29TQ0rWT0qR8hnj/MknE3t0eupW7ltTStup0VTVu5uukGfEkDt7eTNcF1rFv2ajZErqCl3IQr6cGIdOMOLMFjNcDYCLd13UZPaDFls0Sn0URj52acih9RcbjZv5KWaAfdehsVx2FIpmkO91COl3h76EpeFVzMvz3wBU7ufJSA5ue68DqikUbsYhJXWWe6WKGvnKXQe4IQHlqMdiLudgamU9wZWEuPt4Hr67r54LLXUjI8+Bwf7wiuZgi4xegi4m8miEFMmNhunf1mGo9P50TiNGs8bQQ1g5ZgLVO6pMMVY8S2CTgufjCzjylZIqC7WKR72Rrtoix1nuh9lng5w8ZAG1aogVjbGm5ZsoGTQmLZLrYlBvjOwSc4Lk02Isi4NRKruvi2NkX3kjV0yCBd7lpiqzbSGqzHs2IzPe2bWaLV0tWznLq6MB/pvoHlvjre07aa34gtxi683HkkV+X/a++8w+O4znv9Ttm+2IJF7wRAggTYi9hUqEaqd1nuju3YctySOPbNjeNrxzWJWxzLsR1b7rYsy+pdlChKFCn2ChAE0Xtf7C627+zM3D+WkiyLBSSXBMu8z6NHJDF75sPu/Pac852vQH4eqGHoHEasmgVYkSZG0dNR0p4ozQf/RLLjdQhtgXwR9o+irSiG6y+HwDhsPAjqBJHhJ6BMhv1dsGgWNAWhIIGuxaCvEUJBBKeO7h9B9Q8TU52YHYWor7+AEFcR7lyBVl1N6vt/oMUsIgg1aC/uoOymz+KfaCe9/im4bQkMxJlZcRsTbVuIFpoxuRyk+g8zWR7O7LWmSLDfz/qtQ6z+uIZHOJKdKgmMyykGRvYRIIkQiLBr87fQ88yIVXOIumeROvACtpU3w65G1H07id5wJ+mhNmpnr6MwbzZNLz7Mh6vWoSWDKDkC4wMdDCDgqazDo5vp6dtGIO6nsnIZ1pxCLq26igP+A5TJday58mpmik7uH9/FaDJApaucnY0b8ZXVccuKG9mv9NIliTg0ldrkJL2iSFBVqJeKkCpXEdQ0no72Iy9ahSRHSYgqQXOaVSWXcHDni6QLizEVuajVnXx29SfpVKL8JNlDbx50dbfzs1SSWY5Ccl02ZpllenWB5SYfh5Ih9kWGKPDU0CtOsKvrAGOyk6sKayg2e3gguIMKm5X4cCsTNjeC3YpPctGvJdk60oq9tIHhyDi/M7UQFFViisKhzQ9z7ZpPUl01Fz2Z4uf+Dtb5qnkk0s182UmlYuLgeB9r80tZOGcFDwUacaRENh16Ecu8evKEfOR4iJcSCgXuMoZiI2zMzSHlkel45UE6r/g0BWYf87U0OC3IJV4Oh/p4l7uA5ebjR8GcvJDCaSgszJRWCiXQOluhdAZaYpL2h74N0QDpPGcmvTs3nOldWmqCaIKCgBvRUsWwuRPMYA8LWF1F+MWDiFGB4jWfYjzcQdKdgi2bIBAk9+ZPMCEEMI2HUSNpqkpmMTlXZ0IfxOyaT2L3eoSrV6A29yCuWApXrUArmkky3gPV7swh4WQQRW/FFksyy1JJz/A+RF3BLk+l8PcRdJ2JaApXrhlBkggiZPr/hJN0PvUsLaUKlZd9hIMmP8xvQBwagpiGpaaY2kX30CmMI85ahOYuYF7JGrx2DzNFH7/b8T1q1lzPsKRR5Spk2BqnosBOqa2GR1PdFKVTXFK2jA2pFnaPbyTxwnoia++msmQxl5krWYGNXwW7ONTfSFllPdZEijn1y1AtJpaIBXSEOumPDzI61kKrp4CWSDcllgIeOvg4syvm4HAXsjc5jG88SMoK5jwH5dgoktysXvFBYjqMJgKYZBNeMYfdmp/cwhpucM5kX5nApaY8YhYzcQRcopUP2ot5PtzLkC0HwWYhoSZoSvsJS1FCkplfNT2AqbyGgBCjA53Qzte58fKP89Toq/jiVkaK5yIkBtg4Eqbn8BYKltzAcDpIa8cOkl1NHMx7Grl0BodDHTgLS/nNwBbyXEXMkhxIZomi0koimsByyYlJNJE2x1C792NrqCF0eD3jja+hFdXgrVhIYdVcOuLt2OxWwoMHedK/gRXeuRxSVHITabY99VN2lhSyyxylOO3kuarVJ3xMplxpNafjx+hDQzCjhujv/wVGE+R+7vNMNm/CV383o5Eu9NQAjOyDjq5MS5fycky1l+MWqog9+xti3V04338rJXMuJ9HTjF+IUlZ9PeOHDrCg+iq2/uY/qfrge2nf9D8oZjPSVbewwHIZ0VgYoXk/ydoK+sO7UDua0Hq7kbxFqD19kJOEFZdgcc9HsJWRGFwPfd04PXNYteBTDIZ7GXT6CfUfoDxm5ZWlX6FSnso+CZR4mO/93c184D1LsMy5gUXL7qB/dJICwG8WsN9TjnhXBSGPAHlLMQ8pmEpn46m6BFN0FMFsYSDejxIbwS55USUTwmA7ueVVeKQiXIeGkAITXHHzRzkYamHLi78hb+VtXFmynI7RVoqsTh547gc0LL2esFWjd9sG7r32s6REjft33o/Q0kLpmruZiPTjnLWIhY6FtPbsYkCdxCbEielhKk351JU2UKMXsV2YxCeaaQ328iHvMqySiWgyTpMpiSZKOBTojvq5LaeSkBqnRnaxU9LYP9KKLMu0jB2gRLVTnV9GY2cz9y67m19v/y0NhbPZvu151t7297w2vpeBp35JwqkQK7JmCoD292JduRbzgW68ly9ntPMQwkCY5GULUbe9iveKmwg/9QfcC1aR3HWI0jveTVXBfOITIdr7mxnZ9Bi3fPg/ObDnRaQ5S+meaKfUVoZ/89OYKmcyd9EtNG95gYIVl2IxWyk2a7zc+GeS/mbwj1F25cfwdzZSVDybem85WyY7iY8PY5btlFbNp/PBH7Pkqk+hlZTSNLmdcOdWbMV1KJ0hlLVfPeFzMuUZySva0UxFBIaGsC28jnj/DnyKk8joBOk5AfTBrozPv6AY84xSlMJCXHn1zDItYXfL42juKFQ6SUcn0AIBYnGNuMVO575XuHrBe5jprqDtA+9DtjiwuPJQDu6Dhb3sSz+IPZVH9Jkn4NZr0N1m6A+ArxQVAW64JFPnLNpPsqMHs3sWtsVrUP3biI8Osz+wnZFXHkQoK0AYGSJQtILO4BCVeVMQkq7Tvnsr65/cxkuv7OT//IeHSptKP5my27qmE36gFzr7YIkVFoVJzVyGLusM9L0E8SgWzQSpJHpfK8LyW7BZfSSDe4nZooxPvETy9W2s+PDXiepmtmy4n7GuDejldooKlvDnwb3MLVhItZCLU4OqnDl05bxMY2qIkcAANWULcDvrqZl0sKUvRDC5nW3CflwTca5dcSubh3citLVTWOZmjh7nv37yAcR1y/HNnEeweR+vN0hYTRaimsqM3Fk8v+kB/CPtzLzxbr41uheH2YTZ4qXQMYvGyMskNj6FVluHPwJNzgXYigt5KtVJN/10R1JoNS7+1PMocT2Kdd0aHLv3EetrBDWGVFyFI2ln5qLbWFazhudb7qe/MIjNnksk0k/g8J+h/xDKZBzFKTI4sBUpp5Acswf3UJDB4U5eSe0maG5G72jBWbGS4bF9xGvNlFfWsLPvFbwL64lM9tIR6OJAWTHp8RE4eBgO+uld/3Xkq2bjT8m81N6IUuCisKiM2MAAsd3bSG1rpcn7OJGNQ6hVbsyz5xF/+UnMtqop6WPKzoZcRy5hS5jEjqfQhrpAm6Tt8f8k5dTxp1qgdy85lQ2IhfOQ4zqyKBF64ufs2fcDNG8mqJP8HKTCMhIDEWbMux1sedTVXc8ydwNNvXsYeugP9O5/GWU4AE1+1K0b0bwuIm07yLv7NvIsuVh3HQazHVJJxJJCeOrVTJf15hbYcRBBMFOdLOf2y76EIHkZadsCggVdyIGKpSQ7+jMJbSdWETqwff1TjAWSbOmJsWvrJqRIGiuQ8lm47svXIlpl2KpTN1HBPy36NOWaDevEGFbJQV5OJVdUXY8UTiKU1xA5tIX0gY3k4SDcuhN0E3MbrsdlziGsTBAa6gV/grTHwW/8L2OJh4mOtuNIKDS4qkmkE+SZ82j98y+IDHQRCwU4NNrCq6Yhaq/7ILes/AxuXwmlM2YzX85D8Y+x+rK/oeXwdjaEe3AsWYCpuhJ7VGHBlfewrXMTu5/5JYLHwzAJ3CuvwNFQz/5nfkg6HaF/5xN0vPYgB4c2EAoeJLlmFsrurSSfeAxt1yuERw+yN7yL4vlLEYU4opbChEBZ+VzsJhNF11yP99r3YPLVk1++hpnFl1JYu4xkHGYuWMfMFbehDI6BYse0dwK7r57JhcUkZ+YTTgRofvCHNHdt5HDnBqj0EXj4l7C3GWteJdH/vY/44w8j5xYRDnSjPvkQsZFtjCUPk9j1LOmdz2fqLa5eB+9fi/kzd6DnF6H43Fx+6QcwpQQG23cTlCLEHFYkcy5119wDsyow55ehxMMwsx5XrvdEDwpwEjPS/if+DbGwAO25nSRtAlyeB4sXI868Ak3SEHLbsIdzITJJ9JUm7JKMEgujWWQ8rjrC8yyo0ReJ/tezJKsXozQsQU/CQDpCT2wUs9mNubAUdX8nWqUb/v49mUS83mGQrdRWrsJjyeXAyCgDcTM8uRG9aBRGQpkqnB4b1gULcZmKWOldzCttGyhbdDVmXaJV2YFYWs2MpMylt9yCw3Xi7FhdCZOMBHn4iWcZBiwW+O+fbcCa1DPJ3RYR353X4UlOMvH9XSR3+6kdH+fmRVfwaihE3GSh2FVFy0gneUtvoUCTOLT3cdwzGvhSxV38ePRlAqKJNc55rG95Fm18H2bXTLQqK0pURrCnGeppZNA3Rk6FD4ueJtJ6iPcv/gD5S6zsneinqKScPcFWtrZuYKLlBcy5bnCYSZk83Lf991QsuYzc3BnY6hchOez8/d1foynVzca2jayUHJTMu4YdZY3EZZWmyYNMDo1y59wb2eLxkO/Io21GnEtLr2QkMU5rfpSJ0f347vkYWncf6xa8m8Z0jCBhcnHiq74Uj5hDfGSQxufWc931H0DW4vhjfhIRmWigF2vaRVVpA1dYS2m35rA90E9e+TU0b9lPxY1XQzDJjj/9BNvc2aTCo2ivDuO4/BaSL/Yw8+4bad+xkZoPfJy2px8n11yPUOKmUqrkYKCL3A+8m7r8GYzv38PBijIK568hjUA0HSfVkkJLm9E0heqkhcqhIF7RSyw/h2ByDIvHTeG991CJDa1oLi2jraQO7gGvh0W+5dkVEnoK7cebwKsi3Xs52m/a0dVOtMNhBNGCVc9Dry8j2bEVrdhD5LWtCMuWUVL3bqxpN+GnH4Q+P0Vf+Ap6oY+JBx5GEAWUWxqIiBb2j/cRXzkffdAGm1+F0pnQ0Z3p/Voxkx0bf4K8cCUpl4Ull9zLQNkCgl6ZxO5nWdhwG20vrcfpK8XpF3jo0a/jve4GYuufIKTEuGXNvfTk25kY3s+BYDde3+Lj/qpqWqF/y5MM++NsPdSDLMEnbphL41iaA6+3oOogCyIjyQSRMjPSZfl0vzLClz77DVb8513MXHgPE6kkMf8o/a+tR7rmalyal9WrPk6T0MUj9FOQP5P+3n38tPHnOMvmIKSTXLvyy7yWbEKY6CPgkHCsWYMs2okNjrIj2UmZL5dgXENXojz+za9j/8L7sTisyHVzSMZCpPpasVTNZn7eAvYv0Ei4TOwY3cXggR0UzrqULQWdtAdaiQ0Ms68myGpfFXfllPNc+3ZKi6twWlVyBSujw+0MHXiNd19zL0udtXxh/wM482fg8tZTJLhYsOS97OprpNhWRq53BovtebzQuZkqn5srXXMYrg3xymgnVllibuFcei7PoaSokg/a53FYjdGtpRiLxxj3j1A3o4EVf3cfpnSMHz79Ixxrr6PhqrtZLBTSek8PHRNt2L72MQorl9LGEAGHhdqP/AtuZy6zJQ9looPSGREOdjRS5ijDW2qmtXsX0WAbQmERaT1F1Zr30922hfriWSwsWMRALElYsSOOjyFNBilaXMTy/CoefPyHlK27jXeVXcYjoW+TNKnk7+iEVVkUktRwNRbfCkRTkkiRG26QEKoaABPW6uVU25YwOrAPb9mdRJQrsMTTpHK9DO/airlkNkLNJVC/koAs4R2JI1XXI3ld1IUEGopc7Gk+jJQaRLpqNeZb5+AprkEpT6A078Lf2YTaF8a8thCzzcGhsb0oTgml/QDCNbfRn3QhzCpA87joCDcilVQT7X4NbXERnvxFjNnz6Rnchmy1YDGZcZqPX2m1q3kPLf0hXlr/EmlB5wvvrSNVUM88R5RYUwv9QRDCOsu1Kna6KnH8z7WEv/YA/j+0MPirA1j+uZrJ3Cpu9i0h/4b3YcmvwaI7ef7Anyipm4fD5CY12kF498ukC4sQSyoYd0fY0PYkueUNLKu9jfXdD1JgyWOBbx45Tok9yR5ch/wUO3TsJRW8/0vfRi6v5JXhbYz7B+BPf8JUVIylfCG5sptPl1zPs5MH8dvMzL35k4z1NzNTtNIuOZCKK+g4tINUdYQSs5cls9bQ2bOPQtlCueRkSd2lHHS0EJJlvt/xKOEdBxAWaJTVL+Z9RbfSrkeocScpdfrYNNGJYitibmkD3RN9iC4JU10tPlHgWlsdYmCApsA2OgY7eX1ZGc/2bcWRMvHF+htYVLWUw8Fx/HaBxfZyPrP2czwTb8WsmfBYc9GHd1CgioyaNXZEevFcew+iuwibzYtJN9GtJVgg5SP4B6nJqcJhcbOJXqrWvpcq0cWLX/kn9OtL6QpvInfZTdTZ6xjtH2CwKB9zTi41RQ0oY61EOw4wUaAjibm4hQKSZjtpyYSlP8YLP/g9/MN/ZU9IuCqxV1Qhut1ETEGw16H3tJM37yrC6iSt/Y8h9wWYdcmdCGIxg117ScUiUFRI3C1RUHwZy/MXEUol2Xr4ZbS62Xhau5i7bBU71QgpXxEuPQdbwXxuk2t5dXw/5e6ZNE7sgLgO1U4UNYo5OEFuxRJGNjyMvWom8fEg/rad6K9vJ37FaoT6K7B5K4l37Udv3EtwRRlNZhuR7z2AWGShs7oM/53XUGq1H/XXVFIp/ve++/jIR97HF1/bxtK5Vay84062b4ty2fWVvP7sM0hAYjLGT+/7AbZvfZJhaxRteS7ifjP7nthLqQof+tUvMgl/Yg6VYxGesQyjVlahpQX2B/Yw2biF/GXXMBrtx6XLKKqGVlOHYhIZTw8Qi/rpT4RxyQ7y7D5mSV6+uPpaehJjrI8PsbqingNKhJW5izDZnYzfYGPh7Ctoj/XTF+/iavt8ylWZAy0bMeUV8e75tzNXE3lq+EV0JYWtuo7Q7n3o5ZUsnF1HRfVCqoQc5iAw5p2Jz1rGgZZtpOLjrLj6gyyYsYztk430pyfoE/zMyqskrsX5QNFyPJKDaKSLcm8Fa8w+tvdvYmHJQnIEiMaDLJ2xBLs9jy2RJsyildmzV9KT8PObrQ8xo+FKFjqrKcCCy1uB6vAQTkXRVQH3zEXkm03kmyw0x1oY6zyA6B9iVc21REWF3uEeLKUlXFMwm916kFFdx6e6KE65SIlpqj5zL/hszNdK2WYO0TUxQMeXv8G99/2eWF4DPi2Fs6gEPb+APV17uGrJLHZu/Sl65UwWaoUs1ks5aNo9JXlMPWh1LEzYkSQZ7gdNRE5bUYsrmZjopMg3k8FUI+mhg6imO8nXcmhu20u6pgzJlUs+OXi9VdhSOmFVp6K4gY4DG4mIKg+O7kNubicqRRELvcSUCA8/8g3khsWER0MEZB1hzwQsyCW98WW0tIN07iJc9WtJFxeR3PAIpbPWYVv2IVY5Z/DrF36Heb4XLZZL+aIPUuFrIKqacLznH9n48++y+IZV5Fo8x/gldXZvfY3A5CRDna109Y3w6Y/+Hckg2DHjcHkZTr6Vt5ROWVDbBtBNMaoX3Ejht5ah/baJrQ+/zCP/94uUfOvTTFhyOLD5CbzL1rK0fCH9o02E4kEuueIuemJ+7I4capMKqUAcq7WA7eNNrK64nlYll5rayzDJNsyYKRS9fCa4jbb+VoodRcjmAsolK62RfpZ6F7JjvovDqRHuyJtPhxYkqMZ4/cAGxECA5Yvu4JAwzohqItdXQaHsRpFsBNzDuPNriekiveM9LM6bywOT+/FZCtk31oJcu5REvAePr4Gm9oOEkwk6SlT6J8IM2PsJDLfhNdmRCyo5cOhPyKN+Nl97O4oepHu0A7dLI6JDT3cbY7ndKFYrRWXzWB/bycY01DYsw+vx4NMENgiT7EtPMNx+gERslKL6evpGO1GtLspd5czIKYUZccIT47RIYWy6iZSkcV/nBv5l1nXUJRPsiQ5wu3ceKiJ+QSGmRplrKqRYlRF7hpmXYyJ8z7u4VZAoljxYJQ1V0rFO7uB6R4Dmw4eoGsqh/bUuXn9+O+2joyiTsaM/K6cqJIdgxukrZqj/JXj2OQqu+wDx/GJypFLCzY0U9I3hD4Mak9gTaUNacBmrfHMZDIxwo2sOjycm2KkNkW/xovvHuaruKhZ4S3l14CA9RYXYIxEcMYGeR//IyI5tLJ17OTnF9RQ6CmkrLsdalEegaz/p4hlEVI2FuTWMaBByehFy8hnuaudwQxW83oRiy8c2fwE5riKqYhK7ozEOpkex/udnmZAExGP0n1SSce777r/z3vfeRdP6Jyny2rjlrvew5/UnsNoERJPCcOqtLqmVuFm+8qNElSih2Bg1Jpn5DRITox+m5VfP4y7J4fP/+iNSN9Xzq+6d3IMLJX8FP9j+IAM5Yyx1zUaXVGYnHVjKa9mbGGCpvQZPWuGDs29mWJDpVkPoqkRSSNE1MUJN6QLmyx6uk3MI6Ao3eGvZpicgEmGifT/ppfPRVZHt2jizl9/Maxt/SyIxQfdEP37RgX+km9BkFKG6iqRFZTTVhZpyMBLtIp6Os/nwS5QsvYHOQzspmptLMhZlW7qRuDLEdfPeS64mIJkUdgzsRe1vofHAFoRrV6Nv24I60s1wVR65rioa9z2JecVdyFaZwrwqDkc7EfMraU924zblgc1GjVROX882No63YdM16uavZcnMFWyNHsYsu/Dml+EniduZx+hwK05JYHnpAryKmbGeHv5P+RJGpSFo2kasbT9FkUmKFqdYMW8F/bGdfNSbxwxxBsm0hmNONRbJDBXXI4nykYYIOsT7IbqT+cyObAAANRNJREFUPGWE9ge7+J8/NzGpCmRqwlWS6e6dRSFpcwoYTu6HUDtCrol4jkTo9ReQ1n2CRfNvpXL+Lbyw/1nmWDxEwj5idh8Ok5elPg/r2/YjuGUuLVzAhuEDNBTPJDg2iOLIpcTro6SkgPc6Z9Ojx/l2jk4gP4c2e4RLc+xUo6P7VlCJmddGWhlt3008f4zaBXOIhLuwlRXSM7kflBA7kgcRl1XjXLaCwMQ+dvmb2LX9EM7L7yZeakGL9TMu2om4w++o0KnrOi8/9TiNhw8zf+FsNn7nq7xr3RXk1y5G3PosTmeaRGiSCjFTis8EJDqHWKjKWCz5jKUl1jiLCXvgyz/7EV+8/b00/eAZostuxbf2BnK8uVgEB93aBJ9fcg+PxybY1r2XT9dcw4TNxJ5gGz1iDCWd5P5AC+s89QiCicVJmdVWD6WixOujIVYV5lNoK+R7PftZXFLDM6l+TCYdqz2HRXMuZ1RX6Iz1U2+bSViIcOWKO+gXU0wcOsiCedezbt7dVIkWvtG5AcEzgzxHLoeaX6KqYCZDdhNKnoehvibqV76b8dQYUjBElVDMSLGVNqUNjwIH2l6j0luAz1rOwYZLSOfVMlnQhDavDGdMZd2itTw71Mb2gY2IBaXUuMoR0wKyGgJJJD5wmIg6yWsVi5ASY/jySujc+TTBikIWS3NY6qzFIsQZnuxGTSXR4i5usc9AHupkflxgQVEJUnEuuSYX5uIiUrkJTHNWI8syICDFtlMfewgxHEQwzwT3x0CueudDrQdBeAYKFWJhB3/a2IldrSSC6UhhMBNvVTvNkpCiP/wJLJ4BY+2Il14GT+9AcwYZb3qaA/bZdJfNwK+EaQ4PUl84m+3RNsKJKP6hTnomm4kPDiDHgzjtPrYMbCIx3E+b1sFk+06kYJD+lbejVtUTtkRBCRLrO8D22AiVxXXELS42tGwnPNwFg70sWHIrr4UaSWpx3E4XysHdmOYvxRaPIM+aSVodRFbSmZoNN12LKiqor21GXNxAOBgimpd8q57hG79feIJv/+B73HDNSnZv3s5jLWO88N17kWQr0UQQj8+LNjZMgQADEhRI0NbYzKHuLkpn1dCaGMNjyyEqylh9eVRevpCRBzbwzx/9NOt+9AUqbrydYTXCzrifAXzs2b+XvDo3z052EjdZ8GgS7YOtqLEkvvLFPDLZzCL3DLZ0NiJWLiHpskJJBSN6ioQo0B8ZYU9jC8urLyUYG2M83sudBZcgCGbaYwqH40MIsQF2vvISnlU3MWvJWjqVMLWqm/t3Pk4o5ke3FpKcEccaieMo9tA9OQxls3FqNuZ6a2lJqFi9+dRqVqyxPtKKQpGzhHjRDOrNeYy7wiy11xMK60yuvZdJdYDRRx6mfX4LpQX5hJ5eT/LyqxnNLWdm4WxkQaJELKWp9VVSipVcl4fZZZdRpkkMr6glOiLReSCEbIPrS1SSB5qpK62l6+VnMOte/uHfPoHD7czMJra3PjvzX4Z86RGIPoXkmQTETAk5wcY70SHeAnoneirB6y+JPDkaJU6KTIHEN2rrFWdXSJTUwLOH4dJC1Bc3EtjXDTYBPBLy7HL6Wp5Gs8ocHnqZ1p5n0AaH2WS2QTKFZcli1L2HaN3zKEIkht47AvMqCOeZ0NQ02iXXsD88RI5ahDmlkN64idzrb2ZR4XycOXk8fvAR0mP9OOatRDPnsNheyyvjjfSP7Eawytw493ZCFhPN8X4mxQAWwYdgtSEWF2ILJ3DY7MQXzkZuaaWqdgkJPfpX76nO4w89xO59jXzjXz/DD/79+8yrraRyyUpAJ5WIkO8rwaRqmc6IGgzrACqmiRANoounQkNsG+vAZ7bRu/ll/vYLX2TpTWv5xUe/wnNf+D5rckVeK6+hHZECXy2LL72awcgQO8cHUJUJpPFhqpddQyA0gU3V6fb3MOgoIrfQR7vSTSQskE728sxED55oDoOP/Rjv8itoMecyHuqlKtdJ10QfQ4kk/R37yaudhTnYTyQaQNj+Mvl3vJdk1zaejL5KTs1slucsYv/+9Swtvowmm4TJ7cCj+fCZZawTCUiHmC27GBnvoCsaxea0oyaDNDU3Ee09TO6sxdTVLaDQYmdI6aZeLmNLbzcH61ZTrq1gy65h5ld8BGFyNuzL4/LqYkpjKhNjAqNNd1JtkVjnMuNWBIRxuCRegTymM3AAIv5RHtz2ZSzKC4yYRQLRca74+/+Hw+U8cY8qPQi5rsyjraoQCYI5zlHLdYSi0DEOkoWmrRPEdY23+mFARkhTaEXByQhp9kzE1SvQd21EX7AA8iTQ7ZCMMHzwKaguR4yJ0DuGtnc3XHMtuO0wEiHZvDVTumvRIvTHn4aXD2NZs5jkjg1gzkfUPWhdXUTGH0cb64SbrmaiNJcND/4P8275ODz1Kua7byK5/nnybn8/L0y20ffTb8ONq5DnLuLloWZKbAsIt2wn9cpLJGbXI62+HHEkSiIVJdHSC4fb0T0uBtqaiLnmZarCHmFsuJ///P73qa6uxmV1s2NvG//8Tx9DduUBGqgKnoICUol2+tIQ0WFlXQW9MZ323bspWbUKf2CcnqE2InVzcdXMxO/zIV9xHTM/tIdd3/kV2//hR1g+exkpXz59cwL4Jw5AzIbTk090pAPfgmX0BtpQEyoDe7eSMok05bohFiQnFUeIhQkc3k5V7Up6Wh9HL5YZ79vBYlcDq+Zdy4b9z1JQWUrHRB+LFlxGf6CVqMnKZcvuYsQ2gcUeZlRNUlu9jODoIC2Nz+Ea66NFfQRdS1O+QMI1HOJ9s69AEfykulvRJiMoySgzS8qo8cxkbLiPR/e9zupLrsBnsjFTycFrziNmtWIVzNxZcRPBYi+bNlsp7vsQBXaBQlXAMQ7Kw5Dsh0oVlllg23bwRzNn7g57pjp155CAWYCX02H6VYWZ5KInBHJFjasWLkUQT5z2rYcOQ98uBLkf0lH0qA/0Ziiq/CsRCuj2IuKBKOF4mJQ75yijaUD7lORxEiWL7eh2C9LaW0m378gUtk9EocwJVXmQVNEFFd2iZnrE+gehsx2qqjPtEIJhkAdhTgnCZ1Zh1lWSDjvoZmwWC9F4GK2/F0q8MDaJOjnOnHUfpKmtCff869GlckJLYwzvfQU27sI3czFWazlDre3gdRONhVFG+zF/+GOkHn4AIRnG1NVGvHE/8vLF2ExWEnIY/0svEKu/9a03Xtf5+Y9/yKHDHfzb//00r2zagiYKrLvjPW/22hFREWw2xHiCfDME0rBo8SWkJhQObNzEbfd+jBtmLuXBxx5jKE9kwA7dG36ENPcSwhVJHNfPYuTZVuRvPIX7X69C8XtRYlbSs2ahRJtQxloY7hVQX3kJvayY4piX9CUNjG16leIlNyM7TWhOBatcQf+WneAtQxveha1kHrayOja3NzHmH+KV2DBD3T2U5pVRY69g076nmHv1F7BEFQa2HuLamg9zldOMLPbSOOHi7rs/zUDTJsYmYyyiCt2tUJE0EdVzMOUVo3vS2JxOTLIJyWSjrsrH6o8veltpM0EQcNhsmfr1A/Crv4fGHVBiEtgShXYF7BqYk5nG5uUy9AswrmTqrbol0CYgKmR6H2hAimIUKtnHYXSGyTEl0CQVXVMRTtAkTtMhPRZG1oOo6RDjo+Mo/gcov/4KBMl+5DM/cq0jnx1inD2dTbQ7F2Bx5JCMhv96xCnJY+pCKp2BvqsRx4qbCIvdUDoPFBOaOgk9E1DqRg8nM/uSO+5AtDrR9G7oGkQQTEhrLkeIhfA2XMmotplkVRXi/AVom7eR2PNnHKsuJdprg22b4MZb0He3ko4lcCJDvpPg0w/ife89BAY2ovnHKHrP1cStk2i9zWgjMUb9L8MNN6AOdCMtm4vVH6eyYCnN5YNIeS4qy1bQLndQXXops8vf8sT0dbbyv/f/hrIiH3fccgNf/LtPcf2qhZTPnk9mateIBRIIVjeT0RSalnnTLPl5ePM9tO7ewJaJNopHR5gY60S11KNueo3wnw/Br+sQkjqzvvgJDg//F7HdfaR/sZVr/+cK2u0S4wOHqJ+zlEPpSSJKHqnCBnRzguTSJXgKZ5FoKMDf0UPhZZcx3vYqLr0EfdFdRHtbkVctImnNYdPLHdhKvLgKb6JpvUZOej6BsJvxLj/m8Mfp+IML6+AkaxdfwowRNytWQ3FZPbeUNyAiMPuSuxEE6UhCcEYgjuNEUB1rTtBT8MK/wp5nMouhDvFIvwwNesg8jjKwOZ3ZgbyZwH3UsMeMWBRGgAFE3cyBXY3k1EbIX7oOQbRwrDBRzTKLrd1JIlE/sXSIwYDGZPIxLnXOZtHS9xAIRRia6OZg3wGG/X3s3N/F4fYw46P7UUwOMpU0T56pC2nzPmjrIXqpjB5VcV73LpIuSMWDyP2jUFROurUFkklIC+gJEalhJS5HEVpwhJC/B7QkgZgCCRmLqxylvwW6OlFLrMQbQ5mODS1dYHkeUbIycHAjFiQC8TGYm09CTsLsOqT8cuSyQnpefoRZcy/HVzWXpArNkf1ossyMBXfS+m//xME8D6aP3ID69HO0bPgZ3LmYMU81g8lhZprdaGqa+//nvxgYGeff/98/kRieYHdTDz++7xNI5iMHtpqOKZ7ChJVUQiXPCn0pcHg85DtmoLwQZvPB12nrfhHVkYQdmzPt7mstcKgRcXMLylW34Vw3mzKzSOv2Hjb+7dfJX1NNqXk2+d06hcWFrK5bw8utAQ4d2oV+uImQfYKcihXEnosxFvGwfPa70HeOIgdzifWXEIuorLyylECHzPy0RLxPYPSwjk3TyRNkKr3ljHTC0DCEU3b0Z2H1J6CshLd/qwtTfwSOx0gjvPoShG1wOJ6pbP2G6KaUp/M2rMBlwB+ANPGUzmMdY/Q9+QeWD/6O3OLLKGm4DYs1h7SmoOo68VQKf6SbobFBfrR7mPauAIoK0UmITsb40WNfo7r+90wMRwmLKcaax9B0PVPuTSdTNlmNYrLk8J4bP05dVTUPPv1rGlt3Tsniqde1m1VGymEi3b0HUAlvfgoWzIJoHM2Ri97WAS4nYscQoiCgV1ewaNbVdB/aSGD3s7BqGdjcKCNhxLpawptehOoSKPPCnFK0UAwkDaGyBPv8hcyuvZbdW35HqmuE2pveTd/eTfikShKhMYQ5l3BwcDOa20xOZS2tvVuIJsO4qxZirvARH+5Bv2MxVFaQl87BtPw6+mp7cOcWU20pIU/LNBprbz3Ez377J+bWlvPhj93LT775VZy5Hlatu+0v1tM6iWgaHSepdJrdwUwPtaKiUmKpIopzK1ngF+ntHCUZSmI6FES2VZK77haSB3u4518+T+Pzz3KJq4gPf+vd/Orf7scanOR9dX/D7tZRGpKVlFpmUJy2cvvq6xDs70FXrfi7bWzZbOfFphVEDkB5HkghD52dkIjmkQa2PJRpAjKmZpZOEQGKPbCgEnpfgBoPDA+DmoTFq2HOe89MwzGAxhadV2UYtwkk42+8cydCP8qfdd7qUZiEI60yn9z9ElsmAxTs3I0p/hQ5eT/Ame9jIhomLZnxj0cJxCYJdEdJajrpcY40uxBAlEBNMzbSCak0gjcH3e1EqsjHlpeP2ZtPwfJLCI8NcHvBIr7+vg+RYzMxpjRlX0jiYAfs24c443Y0nwXcddAzDKqOPrcaocKFz+ZES6ZwzZlPvlxC38QuJrROWLMcNB3ZNYP0xH5KShcyGA+gFRVn+gW17EPwVmKqKiRdXItaewn7xrugahZqTGL88B5YWMdQz4uklRSyTaNkzuUEJVCENEGzHzXHRsqpI0+OYM+zIzID0e1hVJBQOzqY5a5mrXceVxTNZoajEE1J8dPvf4fRiRD/8bUv47CY2fniy9x9zaV4yyrf+ng1jXAwhoAVLRmh3Ao4bNhSCnmKyvLKauq6xsk1z6Jo3irmzF6MqJuYv3QlWlqhqrgKbe5NyIKAJJpY8cL7EHQdUTaxShAyu+0jT7frSNeDnX+Gr/wjNA9n6uErZPpbH6l6h4dMTZmFeWBLw+zZEIhkmirOvwEKyuEn34LNAYirMAy891qQfJyxNphjcY2WMQ1Vm0r2cYLMEmqSt9qP+Mn8lkNAC5lGZUNvviI0sIvQqJ1OTQBBAbUHhJ63a1EAoaKMustWMDjRy+wVi/AWFRIo8SHpIrLbwZbG18mvn09KjZAY7aOwehaKy0Ng9y7m1a7mG1e9D5coEpmcYNOml6f8+085Q9bAwODYnFxbMgMDg6NiCMnAIAtMeY90rXj3mbTDwOCc5UXtzye8xpiRDAyygCEkA4MsYAjJwCALGEIyMMgChpAMDLKAISQDgyxgCMnAIAsYQjIwyAKGkAwMskB2klEMzm0EAbmokER9KQOXWyh5LYk5kID9h9HT6em27oLAENIFjmi30/L9uXx1zaPck/MUMhLpv1XpSaf49cRK/rh1BZ6DMiXPDpDu6pluc89bppxGYcTanX+IC+vRvjfJk7MfwyIcO08oqStsT5r40PMfZ85XulDHxs6ilec+RqzdRYy4YA4rfrOPF+Y8fVwRAVgEE5dbofXWn7D4xWGEZfPOkpUXDoaQLkBEh4Oi/+3nK/nNJ/U6kyDxjYJGbvz1JkNMJ4khpAuQji/N56flU0+T/ms+4+0h8c0wgsWSRasubAwhXWBItTP49l2/O+Fy7kT8cfbvSVw9tQLyBoaQLjj8q4q4zRE57XGKZSf+htMT48WEIaQLDDWLq7GS63qzN9gFjiGkCwjRaiXvPdl7+C2ycVg7VQwhXUAI5SX8sOah6TbjosQQ0gVEvMaHJ4ufaDhleO2miiGkC4iRpSYKpON3bD8ZJp4tzdpYFzqGkC4gvIdVAurUmgdPBSluFOGdKoaQLiDc+8eZ0KbWz+dEKLqKqJz4OoMMhpAuJIbHuOa5z2VlqN1JKNwwkJWxLgYMIV1AqJOTzPxtklYleuKLT0BrqhA9cvrjXCwYQrrAkPa18Z3htac9zjf334A67s+CRRcHhpAuMLRYjL33z0fRj9pTcsoUeycRTOYsWXXhYwjpAsTdkUKbYhPhY/H72b+n68tLjAjwKWIIyeColMlOtv3N9/C/bzFSnm+6zTnnMYR0AaKZstPf0ivZee6r3+XKV7pp/fkyhKVzEa3WrIx9oWEI6QKk5ybptPOR3iBPcvCF3A66bvw53374flJPFyAsaThzXZ3PUwwhXWgIApIveUaGnm+2sqH+Sb758K9o+9VihKVzDUEdwRDSBYbk8fD1JU+c0XsssZjpXPsLvvnnXzL5bDXqlYtBlM7oPc91DCFdaIgCOVL8rNxqicXM1gWP8N1f/oS2Xy4kfdUSRLv9rNz7XMMQ0ikizp+N/6MrCb97BYJ8cdfZXGix0Ln2Fzz62x/heMGeqUB0kS35Lu4n4BQQrVZa/2Mh1fMG+GzpSwwpXn55+0r0Tgc1fwggDIy8ea2eSqEnkxdNWWC3aOPhmpfY91CS21/5JHU/TKDvPTjdZp0VjEqrJ4GUn0/XJ2fy+Xc/ikN8+4Z+UrMxkPLiTznf/LeDwSJ6BvLI3WJGSoCrK465z0+6p+/M2ej18vEdO7NSAOV0+e1kHt/55bso/0076sjodJtzykyl0qohpJOg999W8aX3/AnpJKMG1CMraFUX2BauZcP6RbhbIe/JFtRAILtGihJ9D82hedXvszvuKaLqGvcFq3n0i2uxPbFjus05JaYiJGNpNxUEgfA9y1l3846TFhHw5mskAS53Hebyuw6T0E08+KFl9IyXAaBpAo5NToo3jqG2tMPUvt/eiaYSnzx3Dk0lQeQfvN0s+6+f8jcrP8XM/2hGDYam26ysYwhpCgiyCc+9vaxwdmRtTKug8Delr8NfZnMvgp0fncGj25dR+ycFU1MPqn8ia/ecTlZbRfa//7/5xBXXMPgvi5Fe2TPdJmUVw2s3BUSbFbucOiv3Wubs4t+v/jNX3LeVjh+XIdXVIrlcZ+XeZxq7aOa3lZv471//DxMfWTnd5mQVQ0hTQK8uY5bz7G6Way0jfHXhU8x7sB3lMTedDywketfyEx58ijk5vGfRub0XaTDb+MNXvkv3N1deMG5yY2l3AuSyUoa+pjLffuY8bcdjkb2HRfYeKAP/Qiffu/wGqh9VkDcfOKpbXTCbWJ3TOg2WnhyzTA4efd/3+VDn5/D9Yut0m3PaGDPSCdCtZopywtNtBgA+KcK31j3Euh+9SuS2JUj5+dNt0mnRYLbxv1/67wuihYwhpBPQd3sxHyw5t74xy00TfPDrT+F8TKPtN4vftodK15VTLgenz7iTZInFzKyftCCXl023KaeFIaTjILlcFKzrn24zjopLjHNH/h7+cekGDn2njvA9Kwi9bwXX/ew15pvPHff3VPhhyU4O/XPpeR1qZRzIHge5spy5j/dl9ijnOCWmAKstGpJwfn43bklofHPtnajtXdNtyjswesieJvG6QmZYzo/GxGHNyrYkp130ZLpYbRVJ/lRF8rin25RTwhDScRifZ8YjZa8E8JkkrNoYSHvZkjDRoUSIaInpNumk+WPdH0kuqZ1uM04JQ0jHQhAIzzy/orY1XWQ47WZnopztScd5J6YCycHwJxPn5dmSIaRjoeuUPwdj6fMzqmAs7aIvnZ064GeTD87acV4mBxpCOg45u/ppipZMtxmnzGGlgJB2drJls8VKRxtCxfn3nhtCOg7pgUE2dsyabjNOGVUX0U41inyaWG5RSBaff6sAQ0gnwLE5e427zjaKLjF8fjrxzjsMIZ2AyZrzb5/xlzSniuhQIiR1o9nRmcQQ0gmQSs4P9/exUHSJfckSdiUl+tMRVP38/mI4Vzl/YzLOAombLuGuum3TbcZpo+gSfYqPgbQXl5jAJKRZaongFm3TbdoFgyGkYyFKqJ8en7b0iTOBposE1YxrOWQK4TbWI1nDeCuPgSAK2EwX5r5CFDSyUxnc4A0MIR0DqbiIma7zI87uZNF0kT2pPEZVo7VltjCEdAzSpbksdPZOtxlnjLBqozmV82YYUUw7OzUpTsRhRcU8cX4dIoOxRzom0mSCgZQXl/X8+1Cnyqiaw+sJ6UixS5kV50AaxiOhJehN536q/F9jzEjHon+YxtD5F6pyMmi6yITqfNOj92LcxvpYJnp8unBLcQTb+edNNIR0DLRYjLByfmWang5vePQmVCcdinfa7PiU9zAtP6g77xpBG0I6Dmnt4nx7YrqFViVKhxI564mCFsHEa9f+gJGPL0XyuM+b9POL80mZArqq0t1ZMN1mTAsJzUSb4uNgqoANcTubErAvmWT8iJfvTEdHlMlOtn3xv7ltaxuhpyoJv3sFclXFGb3n6XJ+yH060HWcHSZYM92GTA9hNbNPiQFB1c4g0JtO4hCT+I504lARyBEVKuXMMswimFD17DgsLIKJj7sH+fiCR9jwTYnP/Ppeyr9+7npRDSEdB+uYTlSzvKOFy8VKTLMQ0yyM8VaagyhoNKdU8qVJrEIKq6BSIul4pakl58W0FHbx+Puhq20qt92xmX0/8mW/e0eWMJZ2x8H3+9389+Erp9uMcxpNF0loJvoUH22pQlpShWxPenk+ZuGVuEhIix93n/WV0eX0p0/sJfxi/g7S9ZXZND2rGDPScdCVFIn4+eU9mm4UXXoznk8UNPanNKrlCMWS/ahLvu8U7QWc7/j38w1jRjI4o0ho9KdtxPXTi5wQEdHlc7coiiGk4yD5cpldMnLiCw2OyXDaQ1Cz05Q6vTBZu2im/d2mc7bCkCGk46DMrWRd/sXRTPhMoOkiii4xqVlJ6Kcfb/77dT9Fu3zhCVvbTAeGkI5DsMZqeOyygKaLpHTpHedPJ1vhaLVV5Hu/+QmDn1t+zs1MhpCOhSih3e7HKlyYOUlnm6BmZ2dSf5uY/t/w5SddxHK+2cr6z36boc+dW03KDCEdA0EUyLVfuJHfZxtNFxlW3fi1+Jti+mHJTpziycczFstOXvj7bzP4+ZXnzDLPENIx0FWVjoHzu5HXuUZCM2VyoPTTXy4XSHae/PS36f3ycuTSEkTr9AYYG0I6BoIkUVN6YWbITicJ3US7cuqziKpr9KYjPB+306zk8Z/v+zU1T4zR8v3509rB0DiQnSbG0i7y5cnpNuOsM6lZsZ+GA0cSRKKa+GYsIMCqnHaWX93JV703kbOxlvydk+gmEWF/K3ry7DiLDCEdA6FhJpfm7c/KWCldYiTtpiVSzNZn5iMlwdeosOybu1ji6M7KPc4XNP30FkExLYXGO50MEhpfW/wkg/O83Lf1akzjMrZVSyh6PYwUjKF19qCn08hlpagjY4iVpejOjBg1s0Qy34p1NIGgqKRybSTyTLibJtDauqdklyGkY9B5t4cPWYdOe5ymeBlP/foySp8dQQhHKR96HQDRauXhfUtYsrr7tO9xMRHSUrSkjp25nCdP8vnVz/O77uWMH8pjdGkOpqiTPFki7bYRc5kwVeQxNs9OuOqvX/32UKXxBT7sQ3lTsssQ0lEQc3KoWX367S6b4mW8+rVVFD36On8dtqklEsz5TojdC6suulkpoZlI6jFEREzCye2XTCdweZsFFZ8UodYzzogjl2iFADqEZvnQ33ypiaNMau9AFyFaOrUmBIaz4SgkVtVxfcHpRTTsjMzg1a+twv7o9mNeo3X28mxX/Wnd53xF0dWTFhFAQtdR9BO/rt45BJa3vr50kYx43vgvyxgz0lHovpvTcgQ8MLyc9CdysB86togAxNoqrqxoP+X7nK8ENTuHlSjzzacmpqnglmOQFuEsdbUxZqS/QpBlrO5T9/Q0xcsI/KAS9VDbCa9N5TsQhYuvqL1DTFIopU5JRFN9tyQ0PAXh05590o6p3dEQ0l8hyDJW86mHBT2wZRX2x44/E73BwBVWVuVcfDOSCZVC6eRLbkW0BGPq1PLDrKKCIJz+dCTFpiYRQ0h/hZZIEOo7tRb1/2/3rcz+0cSUrpU8bhatPXRK9znfCWp2tialk65QpOgaYW1qEQyN0TIAVPPpiWmqWjSEdBTKX9BPOuw/qNqp+jFTWtIBIIjkms/v3kunw4TqJHCSAat20XRSh7mTERtS6uwEthpCOgqO19t5emz+Sb0mplkw+Y2i9CdDm2I76VlpOH3i1UJKl6i2jXFlTRvpnLOzBzWEdBTUiQDRT+WxITh11/QfupaitXVN+XplbhW19osz+/aNginDaQ+7T8KvIyNhFk4svMdHF/HdV69nw865SOGz84gbQjoauo7e3E53JHdKl6d0CZ72oafTU75F/5U2iuTQqVp4QaDoEimm7rmL6MkpL+1MIQk5LE55j3O6GEI6Bno6zfhj5eyNVaIe521K6RL/tv0WCn53EnF5goA+N5wFK89vREHDI059n2RCmlKsnllSz9bx0ZsYQjoOBT/eTtNt5Xzp5TuPKaavbL6N2f/UgxabuuNAriznnro92TLzvCYxhSiFN7CLZiqncFDe6s8/azPRGxhCOh6aSrqnjzn/cpjHRhe948c9qTxm3Z9CHfef3LiiiFM6OY/VhYimi4Q1a9aanB1KlPBioIHAWE5WxjsZjBChKaCGJvF/cylf/WgxRZ5JbLJCUpWJ/roE9/apHb4avBOrqBDVLIyoIcoE6YSRDkldoU99ZzHJN44qAoqdA+MliOGz/1gbQpoKuo75+Z1UvWJFMJtBFLBoOvLkKRZ1jydojxVQ7p7a4e2FSo4Yp1yepEy2TSlcyCKYqJIjhDXrm4l9QdVOc6yEAxOl9B8oAkBSzn5RFENIJ4GWSEDi9Jdk6aFhXm5fyJVLWrJg1fmJishY2oVDSFEopXALJw4ZimkprIKAomce20OJEl4dmUlPfx4kRUzTIKA3MIQ0TaST50b1m7PNWNrFT1suxfuAk4l6Ce/qYe4u38PfultOWFFoSE0R1kyoekaEHdF8elsKMUWnf6tvCGmaKH9cYnKFDZd4cZX8+v7ra5nzhVbUYAjHowKi3c76sqXc94nr+OBVm/hKfvMxX2sRQBXTBFU7P2q6gvSAfVqWcUdj+qV8keJsCzKQmr5erdOF+4AJNXjkIFrX0aJR1MPt1P7jNjZ85VK+P1HNUDrCwdRbXzCqrpHUFQZVC43JYvpTuSgJGSkpcK5koRgz0nQxNIY/5YSLpN/zpGbjJf8cijdOHDOnyPbETl58voDnln6asUV2fvG5H1Bn0uhLayiI9CkFKLqESVAR/OdWux1jRjI4K/y841IiN2toTcdxsOg6WiKBuHkfRT/bzf/56N/xq1AdDlFj8Eiwqiho1NsG0NxpVNvZjl84NoaQpgm9tJB888UTJjQZtZ5U20o9mUR+eTd//td1tKS8WAUFFYGEZuKZiQUIsoYUPzf2R2AIaXoQJQILPJSZL+xzpJQu0afk8ujYYjzPOE5pDNvjO/jc/R+jV8kEECd0MytcHeS446gWHdWio5mmf2YyhDQNhN67jNv/74YLvmXMrsgM/vf5a9n7+ixE5dQf9ticBL/svRRNF5HQKDf5+f68h3j/1a9RPncYNefkcprOBIaQzjaXzGPWp5qpNI9PtyVnnLQuISoCogLullNbxooOB2abgklS+fXAKloTRW9+Aa10tJPWROTg9PvMpt+CiwVRQqqtovPz8OHcxum25oyR0iWGlIxbvzmQCdkRVBCTyjuKZE4FoaoMrcNJ+7gNb1mIpFsmqllQkGiKl1NgDzNU4ga/ZVr3TIaQzgaiRHrNQhZ+bzfvs/dNtzVnlD3RKp7euBR0AUHNVMPSZOi5NY9yhwV958l9iUzO8ZB2aEhRicB4DoO5bn4WuwJZ1JAFjU+UvMILjnkcDBXTub3izPxSU8AQ0hlGsFho/8ZiPrD2VeqyUEv8XEfTBcS08PbCjAIoLp3hlTm4Si/B/tz+qXWJECXC5RJyVAcBtKjErt0z0Z1p0ATMriTtwTwCYTupoIXT71J76hhCOkMIJjNiVRm6w8raNXsvaBGldIn+lA+A0eSxc4FixTrxQonKsTnIrQOgqagTAdCP7oiQnA60N0ISdZAjR7b0sYxk0hGJEbcZ04hpWkUEhpDOCKLVSsuP5/LJSzZiElTy5Qv7vGh/tIInNl6SWc7px68Fp4vQc50NYV0tgiqQvy+N7YkdR702tbiWlPfYg+kiuHKjRANuxLNUdutYGF67M0D86nl8YtmrlJsmKJJDSFMutHt+oiIipgXEdMaxcCJ0GTQTqFYdf72Mdtmio7aujOebOF6JBkGHyaGcaRcRGDNSVpGrqwgtKmTlv+64KNzbkKkzN5xwnfLrFZdOz/VW3LWLKHi+Cy00iRaLIVgsqJbjC0SKC0jxcyMdxRBSlhCtVvRfpPhg0VMXVWrE8yMNtO8tRzyd4AIBQjNhsnoGlqBA6UsBEAQCc6Z/ppkqhpCyhSThMccvGhGpiPyocw3DA15M2VhaCZklXyJPp/tWL9Zx0E9LnWcXQ0jZQJQY+MQCbs99fLotOaMkdBP9qVxUXcSvOBhpzccUy/6sodp0ouVZH/aMYggpC0hOB/nX91/ws9GheAmPvLTyzWQ6+fyZMM44hpCygOCws9zXPd1mnBUEdeqtTi4mDCEZAJlD1YkjNePsYopRxYVFVIiob7ml+2LeM9F+9biIKQEpAWIaUm6dkyjMelYxhJQFtMJc3HLHlK8PqnZypMS0ni+ldImD8TKKzUECaQfrB2cTTZqp8ATpnsglOmFDiElI8b84yDnBYWu2ETSoejKE0NqLnkox+OklJPLOzenQEFIWiJU78cpT6430g46rST5TgJTUWfupLcw/iSDW9mQhii7xx2cvR/GoiDkK5jYbH7n7BQrl0DFbnqiIKLpEWLXx3GgDvQEv8agZYdiKZtdABzEuIqhwWPAg6JkH41xYwknjk6TDYRAEbGM6CR9npCv56WIIKQs4WsYZSnlwWY/vbNgbqyT3CxLqwddBEHjGcSkNn/zTlGem3zx3JTPvH6FmYP+bRfsFWeaRjmsZXyCwcEUbs5yjiH+lgC1j1XT355HjjRGLWRD6bEhHbilO/lXowJGXngvPqsUvoIeOFM3XdfI29qE4yomWZsKDzgkjj2AIKRuMB9gbLKeu6PiBqY+3zqfq4IHMX3Sd0t+38NS7FnBb3t4T3kJFRLXqqG2db/t3PZ3G/fttuB+QiNqsvH7FcgJ1JhJ5+lsBn4AJiI+YMzFh58BMcyyEdGYmdPZAwaOH3irdBaT7+in4+TBSRRma00p4lpuxBSL6OfAUnwMmnP9okSgHByqh6PjXuRwJRKs1U/oYUAMhDgzNmZKQopoF3ZFGys9HHRs7ihEqWjSK5dmdlG52oVeVMHRFLvEi/c14tXPoC/yoyBEBX7NKTuMoWt8g6lFSLfR0mnRnNwCOJglX4wziM7wk3RLjC43EvvMaXUmR85qN4EI7HunYfZISivz2rn66RqrbCfNOfA+XGOeaeYc4cP18cg/kox04DNox9kSTk3BgkqLDFphbS7zYQbBWxj6iYY5ohGbISAkdxXnsB0+1HDFRAs2sox15UqSkgJQEMZX5mS6BqEDaCoigmXR0gXd61wTeNhNKySPeOAWkJHg6FOwtI6S7e6eeSaupqIfbMR8Ge1kpllAJwVoTaSukvPpxA16zjSGkLFH4+yY2vX8Wt+TtO+Y1yYTpr4SkYxue+qd9taeZ1V9sY1d4Bp035B59ZvoL9GQSdh/EChSJ0pvCs4oS6BoIx7636LADIFjMCDYbuiWT8SNE42iTYbR4AsEkI8gyWjyB5HWDLL/ZrUM3yegOK0JaQ82xEKmwYx9KYhoKZsaJxDLjJJIZW3SdqTcOfSfp/gHM/QMUCAKCbCJ240JGlklnTUyGkLKFqqKdIL3MnRN/29IOoHB3Av9HnPikyJRuYxUUbNIpNOb6y9nrjT8fp6O4Fj6SQ3WcVCo9qb6Z6ar6j19azLkt8/8zXu9H19GVFPbn9mMvXUK07OxsCI18pLNIviOCYHt7+xJL+yivTszKNHSeInYpRbq2JNvmXVDoySTFD7VhGzk7+yZDSFlCV9Ls7S877jXtI3nvqDaa7utn/Gsz2BGumfK95lgHkb81hlxUeEq2XiyoY2MU7oydFS+lIaQsoSspHK84jj+z6Ef/dhQVjUOhQvxHaet4LO4s2sPIzdUgnqMxM+cIcmMnntaMU+NMYggpixRtGKUjeZxZ4hihApbWYTrbi9gXrqAzWTCleznEJKY7Rklet9gQ03FQJyfx/nYHvib9jKakG0LKImpbJ7/dseqYP79hZjNy+TuXf1ogiKNb5tWN87l/z6UcThRPac/0/sodhP8uhFx1niXvnG00Feeft1O048xNS4aQsomuY+86tuduy1A1pN/p5NViMfL3pjAHBaRRM7/etYrvtVzLfV1XEtUsxxzPJ0XQNJGJFSc4CTYAXcexfxDT5MnNSs4p9ts2hJRlbGPvXL5Najb8qhNVE9AKjt6lz/ziXiqe8iNHBUiKhPtcDA55iR1HSACXlXYw0SAgLqzPiv0XMun+AUpej0+p0tEb2EenFgdpCCnLuLpSjKXfWVXntcBMnJYUne/yINXOeMfPBUlico4HXQRTSMoUQ0yLqCcI7Kmy+lGtOkOXeZBcp17N52JB2taMOTT1WSnhmZpEDCFlmWSuCbuYJKGbiGoWgqodTRcosISJpkxoJhi/tAjB9PbWjWJtJWOLRDRzZkbTTDqoU//AY8U647c3GI6HE6Ank3g6pj4lTbVHrSGkbKPr7I5UEVTtKLpESLVzIFqOTVIodEZQ7Rr+hTqxGxciV1UgOhwIS+fSe2v+28JZVIeG6FTYEZzBoOKlT8kloZsYVLyoiPhVJ0HV/taMJcBkDaTXLHyHSfKMSlLrliKXlyHa7WfnfTiHce0ZmnLnisnqqY1phAhlGecju9ieewnNdw5hkdJoCEiCRkwxk1IlvJUBdF1guNDKyJJSrONlWEL6O46YTEEJRdTZ2VPJbqkcb04Mq5xmKODCYlGo8ASZTFqJKyYEJfNizQTJXPltH6pcWkJgeTHj8wWc1eUUbbBAe/cxA14vBtLdvZS9nEvv9bYTxuJNNUXDEFK20VQKtofoujoHb04mElw/ohJNFzBJGqomokRNyCaIF+nEi+Box++mgExaEdFSAuO6HdWlIgVlYkDnDAlNE0hGLG/7EOO5Ig5BAF1HMJmZuLwC/zwBBIiUw+hlBfjau8/0u3DOY+6fQFTKUC3ZCXswlnZnAO3AYfL/aGfsYD7Dg16SikwqLZFUMo98Mi0h2dOkPSpph0baoaFLIMcE7IMClgnhzcNDMSkgJTN/l/0yYjpTCETpyEHtciKE5bed86pWAbkyc64kVpaSyBUR02AKCzgGBfJ3BS/q2egNtOFRzMHsjWfMSGcCTcX+2HZmrncQu7KBvrscmO0pbBYFRRUxSSq57igxq4JZTqPrApNdHtI2KNyZJumWQIDxBcLbTuMFPeOEEBUBQcv8Wf6LAo2iAnkHEqS7M4cfWncfnnYfsSITchSK/9hywijtiwUtkSCnXyVemJ25xJiRziBaNIrthX1YOi0kIxaiMQsmScMkaYiCjtOaRFElYgkzmlUDAUYXmbAG0kgpHSkpHDXg8o3EOVF5+8ZKSAuIyltuJkGWCZebMt0fLKDNMCLG30YWg1mNGekMoyspqr67j9At8xleLaE446RViXjcjNORwGFJYTWlGQ+b0cwiCALWTj9WQSCnRSbU4CWeJ6KLAvEC3so01TL/F1SwBATkuA66Tspt4o0jXMFqwRLScPaKSEkdaWjitJLnLjQS3uzNI4aQzgJaLEbOn7bjaZxF2wd8pD0qSDqqLUUqDZKo4y0IE5CcpFQziWofpvW7AHA2gxNAEJALC0g0lKFaRJIeCfuwgvXQAFog+LZkwTdQgyGcf97OGzHlhojejmNYZbImO+duhpDOFrqO2txKzZ/qmZjvIlImoHglLLY0kbgFiymdcRykwdY29s6HXtdJD48gD48gA5YjnjlDHKeOY1sH8qI60vbTX+MZQjqb6Dr63oN49wnk+XI59M1q8EEiaiahW95sHakUeRC6ek44lsHpoYUj5PToxAoEFNeRszzxSNrYG+fcU4xsMIQ0Heg6WiiMs81ELCahWzUERUA3aeiSxNgSBwVbp9vICx89mcTz263k2u0IOU4Eh510gQtNFhlZnokAyd+XhH858ViGkKYJXVWxjeskfQJSUkIX3vr2S+Ry7Pp1BllHi8XgSOVaOjOu7LImN+pkZMpnbob7e7rQNfJ2+JESGRf3Xy4hNLOOYDEf+7UGZxw1GDqpg2tDSNOFrqMeasM8ebSfnXVrDE4TQ0jTia5TsDP2jg2tpxXUEWNZdz5hCGmakbY34+rIFI+X4gL5e3XyXupCV06hCKTBtGE4G6YZXUmR/2gz4s315O6fQDvQYpwNnYcYQjoHUIMhch9vAm36OvhdSAgWC6gquqpO+bxNkGUQRASTjK6kEV1O1MDUHQ6GkM4R3qy1bXBSyMVFaPkehLRGqsBJtNiMmNZJW0V8u/0IEyHSwyPHHUNcWM/ISjeanIlZlBM6oVqRgl1prM/snJod2fhlDAymA8nrRVcU6OhDjUYxp2sYWVqIHIfcliSMBUif4CxOtNvpX+sh6dUBPdPoTMvUzpiolyl94fiNEd7AEJLBeYsaCIAgIPlyIRpF6+nH3ZUHAliGI+ixY/eqegO9vpqk563ln9UvoFohZQbHoIZosx7n1W9hCMng/EbXUcf9mT8mk+Q81wiShDrFpbI0NIGrMwdNBs0k4G1TsIzFGFzjRk5oUx7HEJLBBYU2hVnoL0kPDOL7xVDG0SBlUip0JUVpiwM9paBP0VlhCMnAQNdBV9H/wkOnRaMnNYRxIGtgkAUMIRkYZAFDSAYGWcAQkoFBFjCEZGCQBQwhGRhkAUNIBgZZwBCSgUEWMIRkYJAFDCEZGGQBQ0gGBlnAEJKBQRYwhGRgkAUMIRkYZAFDSAYGWcAQkoFBFjCEZGCQBQwhGRhkAUNIBgZZwBCSgUEWMIRkYJAFDCEZGGQBQ0gGBlnAEJKBQRYwhGRgkAUMIRkYZAFDSAYGWcAQkoFBFjCEZGCQBQR9qn0rDAwMjokxIxkYZAFDSAYGWcAQkoFBFjCEZGCQBQwhGRhkAUNIBgZZwBCSgUEWMIRkYJAFDCEZGGSB/w+MzI8LI7FauwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in post_process, use time:15.616747617721558\n",
      "acl resource release all resource\n",
      "AclLiteModel release source success\n",
      "acl resource release stream\n",
      "acl resource release context\n",
      "Reset acl device  0\n",
      "Release acl resource success\n"
     ]
    }
   ],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565c3b3f-abba-4c71-b74c-d3403b2427af",
   "metadata": {},
   "source": [
    "其中，除了acl相关资源初始化和释放的信息外，“in pre_process, use time”表示前处理耗时，“in inference, use time”表示推理耗时，“in post_process, use time”表示后处理耗时，单位都为秒。"
   ]
  },
  {
   "cell_type": "markdown",
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    "# 样例总结"
   ]
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    "我们来回顾一下以上代码，可以包括以下几个步骤：\n",
    "1. 初始化acl资源：在调用acl相关资源时，必须先初始化AscendCL，否则可能会导致后续系统内部资源初始化出错。  \n",
    "2. 对图片进行前处理：使得模型正常推理。  \n",
    "3. 推理：利用AclLiteModel.execute接口对图片进行推理。  \n",
    "4. 对推理结果进行后处理：使得图片正常画出。  \n",
    "5. 可视化图片：利用plt将结果画出。"
   ]
  }
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